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4-(4-Nitrophenoxy)biphenyl
(2009)
The two phenyl rings of the biphenyl unit of the title compound, C18H13NO3, are almost coplanar [dihedral angle 6.70 (9)°]. The nitrophenyl ring, on the other hand, is significantly twisted out of the plane of the these two rings, making dihedral angles of 68.83 (4)° with the middle ring and 62.86 (4)° with the end ring. The nitro group is twisted by 12.1 (2)° out of the plane of the phenyl ring to which it is attached. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.002 A° ; R factor = 0.040; wR factor = 0.118; data-to-parameter ratio = 12.8.
The six-membered ring of the title compound, C11H16NO, has a distorted envelope conformation. The piperidine N atom deviates by 0.128 (1) Å from the plane through its three neighbouring atoms. In the crystal structure, molecules are connected by intermolecular Cethynyl-H...O contacts to form chains extending in the [10\overline{1}] direction. Key indicators: single-crystal X-ray study; T = 167 K; mean σ(C–C) = 0.001 Å ; R factor = 0.040; wR factor = 0.112; data-to-parameter ratio = 27.3.
Molecules of the title compound, C40H42BrNO6, are located on a crystallographic twofold rotation axis. As a result, the nitro group and bromine residue are mutually disordered with equal occupancies. The propoxy-substituted aromatic rings are close to parallel to each other [dihedral angle = 21.24 (1)°], whereas the propenoxy-substituted rings enclose a dihedral angle of 70.44 (1)°. The dihedral angles between the methylene C atoms and the aromatic rings shows that the propenoxy substituted rings are bent away from the calixarene cavity [dihedral angle between the planes = 35.22 (8)°], whereas the propoxy-substituted rings are almost perpendicular [79.38 (10)°] to the plane of the methylene C atoms. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.006 A° ; disorder in main residue; R factor = 0.065; wR factor = 0.130; data-to-parameter ratio = 11.8.
The asymmetric unit of the title compound, [K(C3H3N2)(C12H24O6)], is composed of a potassium cation bonded to the six O atoms of a crown ether molecule and the two N atoms of a pyrazolate anion. The K...O distances range from 2.8416 (8) to 3.0025 (8) Å, and the two K...N distances are 2.7441 (11) and 2.7654 (11) Å. The K cation is displaced by 0.8437 (4) Å from the best plane through the six O atoms. The latter plane is almost perpendicular to the plane of the pyrazolate ring [dihedral angle 83.93 (3)°]. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.002 A°; R factor = 0.026; wR factor = 0.066; data-to-parameter ratio = 16.5.
The title compound, C14H9Cl3N2OS, has bond lengths and angles which are quite typical for thiourea compounds of this class. The molecule exists in the solid state in its thione form with typical thiourea C=S and C=O bond lengths, as well as shortened C-N bonds. An intramolecular N-H...O hydrogen bond stabilizes the molecular conformation. Intermolecular N-H...S hydrogen bonds link the molecules to form centrosymmetric dimers. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.002 A° ; R factor = 0.029; wR factor = 0.078; data-to-parameter ratio = 17.2.
In the title compound, C30H34N2O6, the complete molecule is generated by a crystallographic 2/m symmetry operation. The 1-oxyl-3-pyrroline-3-carboxylate group lies on a mirror plane. The dihedral angle between the ring planes of the biphenyl fragment is constrained by symmetry to be zero, resulting in rather short intramolecular H...H contact distances of 2.02 Å. In the crystal, molecules are connected along the a-axis direction by very weak intermolecular methyl-phenyl C-H...[pi] interactions. The C-H bond is not directed to the center of the benzene ring, but mainly to one C atom [C-H...C(x - 1, y, z): H...C = 2.91 Å and C-H...C = 143°]. Key indicators: single-crystal X-ray study; T = 169 K; mean σC–C) = 0.002 Å ; R factor = 0.049; wR factor = 0.126; data-to-parameter ratio = 19.8.
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. ...
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.