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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. ...
Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries.
There is a renewed interest in pseudoreceptor models which enable computational chemists to bridge the gap of ligand- and receptor-based drug design. We developed a pseudoreceptor model for the histamine H4 receptor (H4R) based on five potent antagonists representing different chemotypes. Here we present the selection of potential ligand binding pockets that occur during molecular dynamics (MD) simulations of a homology-based receptor model. We present a method for prioritizing receptor models according to their match with the consensus ligand-binding mode represented by the pseudoreceptor. In this way, ligand information can be transferred to receptor-based modelling. We use Geometric Hashing to match three-dimensional points in Cartesion space. This allows for the rapid translation- and rotation-free comparison of atom coordinates, which also permits partial matching. The only prerequisite is a hash table, which uses distance triplets as hash keys. Each time a distance triplet occurring in the candidate point set which corresponds to an existing key, the match is represented by a vote of the respective key. Finally, the global match of both point sets can be easily extracted by selection of voted distance triplets. The results revealed a preferred ligand-binding pocket in H4R, which would not have been identified using an unrefined homology model of the protein. The key idea was to rely on ligand information by pseudoreceptor modelling.
We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D, MOE pharmacophore quadruples) in retrospective virtual screenings using the COBRA collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list.
The representation of small molecules as molecular graphs is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis. We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker algorithm for characterization of binding site volumes and employs a Growing Neural Gas procedure to derive graph representations of pocket topologies. Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes.
The CUG-binding protein 1 (CUG-BP1) is a member of the CUG-BP1 and ETR-like factors (CELF) family or the Bruno-like family and is involved in the control of splicing, translation and mRNA degradation. Several target RNA sequences of CUG-BP1 have been predicted, such as the CUG triplet repeat, the GU-rich sequences and the AU-rich element of nuclear pre-mRNAs and/or cytoplasmic mRNA. CUG-BP1 has three RNA-recognition motifs (RRMs), among which the third RRM (RRM3) can bind to the target RNAs on its own. In this study, we solved the solution structure of the CUG-BP1 RRM3 by hetero-nuclear NMR spectroscopy. The CUG-BP1 RRM3 exhibited a noncanonical RRM fold, with the four-stranded b-sheet surface tightly associated with the N-terminal extension. Furthermore, we determined the solution structure of the CUG-BP1 RRM3 in the complex with (UG)3 RNA, and discovered that the UGU trinucleotide is specifically recognized through extensive stacking interactions and hydrogen bonds within the pocket formed by the b-sheet surface and the N-terminal extension. This study revealed the unique mechanism that enables the CUG-BP1 RRM3 to discriminate the short RNA segment from other sequences, thus providing the molecular basis for the comprehension of the role of the RRM3s in the CELF/Bruno-like family.
Jeder Mensch kämpft täglich erfolgreich mit Krankheitserregern, ohne dass er sich der komplexen molekularen Vorgänge dabei bewusst wäre. Wie in einem Hollywood-Streifen geht es rasant zur Sache. Ist das Immunsystem angeschlagen oder trifft es auf starke Gegner, kann eine Infektion binnen weniger Tage außer Kontrolle geraten und lebensbedrohliche Reaktionen hervorrufen. Der menschliche Organismus benötigt eine effiziente Verteidigungsstrategie gegen die Eindringlinge und muss, ebenso wie der britische Geheimdienst im Bond-Film, in die Ausbildung geübter Agenten investieren, Agenten mit Doppel-Null-Status. Agenten wie James Bond.
Antibiotika-Resistenz: Die Tricks der Bakterien : Pumpsysteme werfen die Arzneistoffe aus der Zelle
(2009)
Immer häufiger sind Bakterien resistent gegen ein bestimmtes Antibiotikum, oft auch gleich gegen mehrere. Eine Infektion, die von solchen multiresistenten Bakterien verursacht wird, kann nicht mehr mit Antibiotika bekämpft werden. Im schlimmsten Fall führt sie bei immungeschwächten Patienten zum Tod. Um zielgerichtet neue und wirkungsvolle Medikamente entwickeln zu können, ist es wichtig zu wissen, wie die Bakterienzelle sich gegen die Zerstörung durch Antibiotika wehrt. Ein inzwischen genau entschlüsselter Mechanismus ist die Efflux-Pumpe, die für die Zelle schädliche Substanzen wieder hinausbefördert.
Riboswitches are a novel class of genetic control elements that function through the direct interaction of small metabolite molecules with structured RNA elements. The ligand is bound with high specificity and affinity to its RNA target and induces conformational changes of the RNA's secondary and tertiary structure upon binding. To elucidate the molecular basis of the remarkable ligand selectivity and affinity of one of these riboswitches, extensive all-atom molecular dynamics simulations in explicit solvent ({approx}1 µs total simulation length) of the aptamer domain of the guanine sensing riboswitch are performed. The conformational dynamics is studied when the system is bound to its cognate ligand guanine as well as bound to the non-cognate ligand adenine and in its free form. The simulations indicate that residue U51 in the aptamer domain functions as a general docking platform for purine bases, whereas the interactions between C74 and the ligand are crucial for ligand selectivity. These findings either suggest a two-step ligand recognition process, including a general purine binding step and a subsequent selection of the cognate ligand, or hint at different initial interactions of cognate and noncognate ligands with residues of the ligand binding pocket. To explore possible pathways of complex dissociation, various nonequilibrium simulations are performed which account for the first steps of ligand unbinding. The results delineate the minimal set of conformational changes needed for ligand release, suggest two possible pathways for the dissociation reaction, and underline the importance of long-range tertiary contacts for locking the ligand in the complex.
Shape complementarity is a compulsory condition for molecular recognition. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment with fuzzy pharmacophore scoring. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified...