Supramolecular modification of ABC triblock terpolymers in confinement assembly

The self-assembly of AB diblock copolymers in three-dimensional (3D) soft confinement of nanoemulsions has recently become an attractive bottom up route to prepare colloids with controlled inner morphologies. In that regard, ABC triblock terpolymers show a more complex morphological behavior and could thus give access to extensive libraries of multicompartment microparticles. However, knowledge about their self-assembly in confinement is very limited thus far. Here, we investigated the confinement assembly of polystyrene-block-poly(4-vinylpyridine)-block-poly(tert-butyl methacrylate) (PS-b-P4VP-b-PT or SVT) triblock terpolymers in nanoemulsion droplets. Depending on the block weight fractions, we found spherical microparticles with concentric lamella–sphere (ls) morphology, i.e., PS/PT lamella intercalated with P4VP spheres, or unusual conic microparticles with concentric lamella–cylinder (lc) morphology. We further described how these morphologies can be modified through supramolecular additives, such as hydrogen bond (HB) and halogen bond (XB) donors. We bound donors to the 4VP units and analyzed changes in the morphology depending on the binding strength and the length of the alkyl tail. The interaction with the weaker donors resulted in an increase in volume of the P4VP domains, which depends upon the molar fraction of the added donor. For donors with a high tendency of intermolecular packing, a visible change in the morphology was observed. This ultimately caused a shape change in the microparticle. Knowledge about how to control inner morphologies of multicompartment microparticles could lead to novel carbon supports for catalysis, nanoparticles with unprecedented topologies, and potentially, reversible shape changes by light actuation

Statistical analysis on high-dimensional spheres and shape spaces

We consider the statistical analysis of data on high-dimensional spheres and shape spaces. The work is of particular relevance to applications where high-dimensional data are available--a commonly encountered situation in many disciplines. First the uniform measure on the infinite-dimensional sphere is reviewed, together with connections with Wiener measure. We then discuss densities of Gaussian measures with respect to Wiener measure. Some nonuniform distributions on infinite-dimensional spheres and shape spaces are introduced, and special cases which have important practical consequences are considered. We focus on the high-dimensional real and complex Bingham, uniform, von Mises-Fisher, Fisher-Bingham and the real and complex Watson distributions. Asymptotic distributions in the cases where dimension and sample size are large are discussed. Approximations for practical maximum likelihood based inference are considered, and in particular we discuss an application to brain shape modeling.Comment: Published at http://dx.doi.org/10.1214/009053605000000264 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections

Improved approximation of arbitrary shapes in dem simulations with multi-spheres

DEM simulations are originally made for spherical particles only. But most of real particles are anything but not spherical. Due to this problem, the multi-sphere method was invented. It provides the possibility to clump several spheres together to create complex shape structures. The proposed algorithm offers a novel method to create multi-sphere clumps for the given arbitrary shapes. Especially the use of modern clustering algorithms, from the field of computational intelligence, achieve satisfactory results. The clustering is embedded into an optimisation algorithm which uses a pre-defined criterion. A mostly unaided algorithm with only a few input and hyperparameters is able to approximate arbitrary shapes