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The title compound, C14H20O3, is a synthetic analogue with a long aliphatic side chain of the important food additive and flavoring agent, vanillin. There are two independent molecules in the asymmetric unit, each having an essentially planar conformation (r.m.s. deviations of 0.023 and 0.051Å for all non-H atoms of the two molecules in the asymmetric unit). Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.002 A°; R factor = 0.049; wR factor = 0.144; data-to-parameter ratio = 15.9.
The title compound, C20H22O2, crystallizes with two independent molecules in the asymmetric unit. In each molecule, all the non-H atoms lie in a common plane (r.m.s. deviations of 0.098 and 0.079 Å). There is a [pi]-[pi] stacking interaction in the crystal structure. The central aromatic rings of the two molecules, which are stacked head-to-tail one above the other, are separated by centroid-to-centroid distances of 3.872 (13) and 3.999 (10) Å. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.003 A° ; R factor = 0.044; wR factor = 0.101; data-to-parameter ratio = 14.6.
In the title compound, C11H14O4, an intermediate for the synthesis of a new kind of estrogen receptor modulator, all non-H atoms lie on a common plane (r.m.s. deviation = 0.0472 Å). All C-C bonds in the side chain are in a trans conformation, and the hydroxyl group is also trans to the methylene chain. In the crystal structure, molecules form centrosymmetric dimers showing a head-to-head arrangement which is stabilized by O-H...O hydrogen bonds. A weak C-H...O contact is also present.
9,9-Dimethyl-9-silafluorene
(2009)
The title compound, C14H14Si, crystallizes with two almost identical molecules (r.m.s. deviation = 0.080 Å for all non-H atoms) in the asymmetric unit. All atoms of the silafluorene moiety lie in a common plane (r.m.s. deviations = 0.049 and 0.035 Å for the two molecules in the asymmetric unit). The Si-Cmethyl bonds are significantly shorter [1.865 (4)-1.868 (4) Å] than the Si-Caromatic bonds [1.882 (3)-1.892 (3) Å]. Owing to strain in the five-membered ring, the endocyclic C-Si-C angles are reduced to 91.05 (14) and 91.21 (14)°. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.005 A°; R factor = 0.061; wR factor = 0.157; data-to-parameter ratio = 16.3.
The complete molecule of the title compound, C18H24N2O2, is generated by a crystallographic inversion centre. The torsion angles in the hexamethylene chain are consistent with an antiperiplanar conformation, whereas the conformation of the O—CH2—CH2—CH2 unit is gauche. The three-dimensional crystal packing is stabilized by N—H⋯O and N—H⋯N hydrogen bonding.
The Mg centre in the title compound, [MgBr2(C2H7N)3], is pentacoordinated in a trigonal-bipyramidal mode with the two Br atoms in axial positions and the N atoms of the dimethylamine ligands in equatorial positions. The MgII centre is located on a crystallographic twofold rotation axis. The crystal structure is stabilized by N—H⋯Br hydrogen bonds. The N atom and H atoms of one dimethylamine ligand are disordered over two equally occupied positions.
Modelling protein flexibility and plasticity is computationally challenging but important for understanding the function of biological systems. Furthermore, it has great implications for the prediction of (macro) molecular complex formation. Recently, coarse-grained normal mode approaches have emerged as efficient alternatives for investigating large-scale conformational changes for which more accurate methods like MD simulation are limited due to their computational burden. We have developed a Normal Mode based Simulation (NMSim) approach for efficient conformation generation of macromolecules. Combinations of low energy normal modes are used to guide a simulation pathway, whereas an efficient constraints correction approach is applied to generate stereochemically allowed conformations. Non-covalent bonds like hydrogen bonds and hydrophobic tethers and phi-psi favourable regions are also modelled as constraints. Conformations from our approach were compared with a 10 ns MD trajectory of lysozyme. A 2-D RMSD plot shows a good overlap of conformational space, and rms fluctuations of residues show a correlation coefficient of 0.78 between the two sets of conformations. Furthermore, a comparison of NMSim simulations starting from apo structures of different proteins show that ligand-bound conformations can be sampled for those cases where conformational changes are mainly correlated, e.g., domain-like motion in adenylate kinase. Efforts are currently being made to also model localized but functionally important motions for protein binding pockets and protein-protein interfaces using relevant normal mode selection criteria and implicit rotamer basin creation.
A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers.
Protein kinases are targets for drug development. Dysregulation of kinase activity leads to various diseases, e.g. cancer, inflammation, diabetes. Human polo-like kinase 1 (Plk1), a serine/threonine kinase, is a cancer-relevant gene and a potential drug target which attracts increasing attention in the field of cancer therapy. Plk1 is a key player in mitosis and modulates entry into mitosis and the spindle checkpoint at the meta-/anaphase transition. Plk1 overexpression is observed in various human tumors, and it is a negative prognostic factor for cancer patients. The same catalytical mechanism and the same co-substrate (ATP) lead to the problem of inhibitor selectivity. A strategy to solve this problem is represented by targeting the inactive conformation of kinases. Kinases undergo conformational changes between active and inactive conformation and thus an additional hydrophobic pocket is created in the inactive conformation where the surrounding amino acids are less conserved. A "homology model" of the inactive conformation of Plk1 was constructed, as the crystal structure in its inactive conformation is unknown. A crystal structure of Aurora A kinase served as template structure. With this homology model a receptor-based pharmacophore search was performed using SYBYL7.3 software. The raw hits were filtered using physico-chemical properties. The resulting hits were docked using Gold3.2 software, and 13 candidates for biological testing were manually selected. Three compounds of the 13 tested exhibit anti-proliferative effects in HeLa cancer cells. The most potent inhibitor, SBE13, was further tested in various other cancer cell lines of different origins and displayed EC50 values between 12 microM and 39 microM. Cancer cells incubated with SBE13 showed induction of apoptosis, detected by PARP (Poly-Adenosyl-Ribose-Polymerase) cleavage, caspase 9 activation and DAPI staining of apoptotic nuclei.
For a virtual screening study, we introduce a combination of machine learning techniques, employing a graph kernel, Gaussian process regression and clustered cross-validation. The aim was to find ligands of peroxisome-proliferator activated receptor gamma (PPAR-y). The receptors in the PPAR family belong to the steroid-thyroid-retinoid superfamily of nuclear receptors and act as transcription factors. They play a role in the regulation of lipid and glucose metabolism in vertebrates and are linked to various human processes and diseases. For this study, we used a dataset of 176 PPAR-y agonists published by Ruecker et al. ...