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Human Transformer2-beta (hTra2-beta) is an important member of the serine/arginine-rich protein family, and contains one RNA recognition motif (RRM). It controls the alternative splicing of several pre-mRNAs, including those of the calcitonin/calcitonin gene-related peptide (CGRP), the survival motor neuron 1 (SMN1) protein and the tau protein. Accordingly, the RRM of hTra2-beta specifically binds to two types of RNA sequences [the CAA and (GAA)2 sequences]. We determined the solution structure of the hTra2-beta RRM (spanning residues Asn110–Thr201), which not only has a canonical RRM fold, but also an unusual alignment of the aromatic amino acids on the beta-sheet surface. We then solved the complex structure of the hTra2-beta RRM with the (GAA)2 sequence, and found that the AGAA tetra-nucleotide was specifically recognized through hydrogen-bond formation with several amino acids on the N- and C-terminal extensions, as well as stacking interactions mediated by the unusually aligned aromatic rings on the beta-sheet surface. Further NMR experiments revealed that the hTra2-beta RRM recognizes the CAA sequence when it is integrated in the stem-loop structure. This study indicates that the hTra2-beta RRM recognizes two types of RNA sequences in different RNA binding modes.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 Protein kinases are important targets for drug development. The almost identical protein folding of kinases and the common co-substrate ATP leads to the problem of inhibitor selectivity. Type II inhibitors, targeting the inactive conformation of kinases, occupy a hydrophobic pocket with less conserved surrounding amino acids. Human polo-like kinase 1 (Plk1) represents a promising target for approaches to identify new therapeutic agents. Plk1 belongs to a family of highly conserved serine/threonine kinases, and is a key player in mitosis, where it modulates the spindle checkpoint at metaphase/anaphase transition. Plk1 is over-expressed in all today analyzed human tumors of different origin and serves as a negative prognostic marker in cancer patients. The newly identified inhibitor, SBE13, a vanillin derivative, targets Plk1 in its inactive conformation. This leads to selectivity within the Plk family and towards Aurora A. This selectivity can be explained by docking studies of SBE13 into the binding pocket of homology models of Plk1, Plk2 and Plk3 in their inactive conformation. SBE13 showed anti-proliferative effects in cancer cell lines of different origins with EC50 values between 5 microM and 39 microM and induced apoptosis. Increasing concentrations of SBE13 result in increasing amounts of cells in G2/M phase 13 hours after double thymidin block of HeLa cells. The kinase activity of Plk1 was inhibited with an IC50 of 200 pM. Taken together, we could show that carefully designed structure-based virtual screening is well-suited to identify selective type II kinase inhibitors targeting Plk1 as potential anti-cancer therapeutics.
In contrast to the previous structure determinations of the title structure, (NH4)2[MoS4], the present determination at 173 K localized the positions of the H atoms. The title structure belongs to the beta-K2SO4 family and all the ions are located on crystallographic mirror planes. The ions are held together by N—H ... S hydrogen bonds (some of which are bifurcated), forming a three-dimensional network. One of the N atoms has nine contacts to the S atoms shorter than 4 Å, and the other has ten.
The title compound. C15H14N2O4, (I), has a gauche–gauche (O/C/C/C—O/C/C/C or GG) conformation and is a positional isomer of propane-1,3-diyl bis(pyridine-3-carboxylate), (II). The molecule of (I) lies on a twofold rotation axis, which passes through the central C atom of the aliphatic chain, giving one half-molecule per asymmetric unit. There is excellent agreement of the geometric parameters of (I) and (II). The most obvious differences between them are the O/C/C/C—O/C/C/C torsion angles [56.6 (2)° in (I) and 174.0 (3)/70.2 (3)° in (II) for GG and TG conformations, respectively] and the dihedral angle between the planes of the aromatic rings [80.3 (10)° in (I) and 76.5 (3)° in (II)]. The crystal structure is stabilized by weak C—H ... N and C—H ... O hydrogen bonding.
The title compound, C15H14N2O4, has a trans–gauche [O/C/C/C–O/C/C/C] (TG) conformation. The angle between the planes of aromatic rings is 76.4 (3)°. The crystal structure is stabilized by van der Waals interactions and C—H ... O hydrogen bonds. The crystal used was a non-merohedral twin with a fractional contribution of the minor component of 0.443 (5).
The continuous progress in the structural and functional characterization of aquaporins increasingly attracts attention to study their roles in certain mammalian diseases. Although several structures of aquaporins have already been solved by crystallization, the challenge of producing sufficient amounts of functional proteins still remains. CF (cell free) expression has emerged in recent times as a promising alternative option in order to synthesize large quantities of membrane proteins, and the focus of this report was to evaluate the potential of this technique for the production of eukaryotic aquaporins. We have selected the mouse aquaporin 4 as a representative of mammalian aquaporins. The protein was synthesized in an E. coli extract based cell-free system with two different expression modes, and the efficiencies of two modes were compared. In both, the P-CF (cell-free membrane protein expression as precipitate) mode generating initial aquaporin precipitates as well as in the D-CF (cell-free membrane protein expression in presence of detergent) mode, generating directly detergent solubilized samples, we were able to obtain mg amounts of protein per ml of cell-free reaction. Purified aquaporin samples solubilized in different detergents were reconstituted into liposomes, and analyzed for the water channel activity. The calculated Pf value of proteoliposome samples isolated from the D-CF mode was 133 µm/s at 10°C, which was 5 times higher as that of the control. A reversible inhibitory effect of mercury chloride was observed, which is consistent with previous observations of in vitro reconstituted aquaporin 4. In this study, a fast and convenient protocol was established for functional expression of aquaporins, which could serve as basis for further applications such as water filtration.
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, [Li3(C4F9O)3(C3H6O)3], features an open Li/O cube with an Li ion missing at one corner. Three of the four bridging O atoms of the cube carry a fluorinated tert-butyl residue, whereas the fourth is part of an acetone molecule. Two of the Li atoms are further bonded to a non-bridging acetone molecule. Two of the lithium ion coordination geometries are very distorted LiO4 tetrahedra; the third could be described as a very distorted LiO3 T-shape with two distant F-atom neighbours. The Li[cdots, three dots, centered]Li contact distances for the three-coordinate Li+ ion [2.608 (14) and 2.631 (12) Å] are much shorter that the contact distance [2.940 (13) Å] between the tetrahedrally coordinated species.
Experimental results are presented for 180 in silico designed octapeptide sequences and their stabilizing effects on the major histocompatibility class I molecule H-2Kb. Peptide sequence design was accomplished by a combination of an ant colony optimization algorithm with artificial neural network classifiers. Experimental tests yielded nine H-2Kb stabilizing and 171 nonstabilizing peptides. 28 among the nonstabilizing octapeptides contain canonical motif residues known to be favorable for MHC I stabilization. For characterization of the area covered by stabilizing and non-stabilizing octapeptides in sequence space, we visualized the distribution of 100,603 octapeptides using a self-organizing map. The experimental results present evidence that the canonical sequence motives of the SYFPEITHI database on their own are insufficient for predicting MHC I protein stabilization.
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.