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The central structural element of the title compound, C24H29NO2, is a carbazole unit substituted with two acetyl residues and an octyl chain. The acetyl residues are nearly coplanar [dihedral angles = 5.37 (14) and 1.0 (3)°] with the carbazole unit which is essentially planar (r.m.s. deviation for all non-H atoms = 0.025 Å). The octyl chain adopts an all-trans conformation. The crystal packing is stabilized by C—H ... O hydrogen bonds.
The dihydropyrimidine ring of the title compound, C13H15ClN2S, adopts an envelope conformation with five almost coplanar atoms (r.m.s. deviation = 0.054 Å) and the C atom bearing the two methyl substituents deviating from this plane by 0.441 (2) Å. The best plane through the five almost coplanar atoms forms a dihedral angle of 89.56 (5)° with the benzene ring. The crystal packing is characterized by centrosymmetric dimers connected by pairs of N—H ... S hydrogen bonds.
The two rings in the title compound, C11H12N2O4S, are roughly coplanar [dihedral angle = 6.77 (8)°]. Whereas the two outer methyl groups of the three methoxy groups are almost coplanar with the aromatic ring to which they are attached [C—C—O—C torsion angles = 8.5 (3) and -8.3 (3)°], the methyl group of the central methoxy substituent is not [C—C—C—C = -78.4 (3)°]. The crystal packing is stabilized by N—H ... O hydrogen bonding.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
In prokaryotes, RNA thermometers regulate a number of heat shock and virulence genes. These temperature sensitive RNA elements are usually located in the 5'-untranslated regions of the regulated genes. They repress translation initiation by base pairing to the Shine–Dalgarno sequence at low temperatures. We investigated the thermodynamic stability of the temperature labile hairpin 2 of the Salmonella fourU RNA thermometer over a broad temperature range and determined free energy, enthalpy and entropy values for the base-pair opening of individual nucleobases by measuring the temperature dependence of the imino proton exchange rates via NMR spectroscopy. Exchange rates were analyzed for the wild-type (wt) RNA and the A8C mutant. The wt RNA was found to be stabilized by the extraordinarily stable G14–C25 base pair. The mismatch base pair in the wt RNA thermometer (A8–G31) is responsible for the smaller cooperativity of the unfolding transition in the wt RNA. Enthalpy and entropy values for the base-pair opening events exhibit linear correlation for both RNAs. The slopes of these correlations coincide with the melting points of the RNAs determined by CD spectroscopy. RNA unfolding occurs at a temperature where all nucleobases have equal thermodynamic stabilities. Our results are in agreement with a consecutive zipper-type unfolding mechanism in which the stacking interaction is responsible for the observed cooperativity. Furthermore, remote effects of the A8C mutation affecting the stability of nucleobase G14 could be identified. According to our analysis we deduce that this effect is most probably transduced via the hydration shell of the RNA.
We present here a set of 13C-direct detected NMR experiments to facilitate the resonance assignment of RNA oligonucleotides. Three experiments have been developed: (1) the (H)CC-TOCSY-experiment utilizing a virtual decoupling scheme to assign the intraresidual ribose 13C-spins, (2) the (H)CPC-experiment that correlates each phosphorus with the C40 nuclei of adjacent nucleotides via J(C,P) couplings and (3) the (H)CPC-CCH-TOCSY-experiment that correlates the phosphorus nuclei with the respective C10,H10 ribose signals. The experiments were applied to two RNA hairpin structures. The current set of 13C-direct detected experiments allows direct and unambiguous assignment of the majority of the hetero nuclei and the identification of the individual ribose moieties following their sequential assignment. Thus, 13C-direct detected NMR methods constitute useful complements to the conventional 1H-detected approach for the resonance assignment of oligonucleotides that is often hindered by the limited chemical shift dispersion. The developed methods can also be applied to large deuterated RNAs. Keywords: NMR spectroscopy , Direct carbon , detection , RNA
The asymmetric unit of the title compound, [K(C5HF6N2)(H2O)2]n, is composed of two 3,5-bis(trifluoromethyl)pyrazolide anions, two potassium cations and four water molecules. The water molecules and 3,5-bis(trifluoromethyl)pyrazolide anions act as bridges between the potassium cations. Each potassium cation is surrounded by four O atoms [K—O = 2.705 (3)–2.767 (3) Å] and four F atoms [K—F = 2.870 (7)–3.215 (13) Å]. The water molecules and the 3,5-bis(trifluoromethyl)pyrazolide anions are connected by O—H ... N hydrogen bonds, forming layers in the ab plane. All –CF3 groups show rotational disorder between two orientations each.
The title compound, [Na(CF3O3S)(C12H24O6)], features a sodium cation that is coordinated by eight O atoms in an irregular hexagonal bipyramidal environment. The equatorial positions are occupied by the six O atoms of an 18-crown-6 ether ring. In the axial positions, there is one O atom of a trifluoromethanesulfonate anion and an ether O atom of a symmetry-equivalent crown ether ring. In this way, centrosymmetric dimers are formed.