Rational Design of Potent Inhibitors of a Metallohydrolase Using a Fragment-Based Approach

Metallohydrolases form a large group of enzymes that have fundamental importance in a broad range of biological functions. Among them, the purple acid phosphatases (PAPs) have gained attention due to their crucial role in the acquisition and use of phosphate by plants and also as a promising target for novel treatments of bone-related disorders and cancer. To date, no crystal structure of a mammalian PAP with drug-like molecules bound near the active site is available. Herein, we used a fragment-based design approach using structures of a mammalian PAP in complex with the Maybridge^TM fragment CC063346, the amino acid L-glutamine and the buffer molecule HEPES, as well as various solvent molecules to guide the design of highly potent and efficient mammalian PAP inhibitors. These inhibitors have improved aqueous solubility when compared to the clinically most promising PAP inhibitors available to date. Furthermore, drug-like fragments bound in newly discovered binding sites mapped out additional scaffolds for further inhibitor discovery, as well as scaffolds for the design of inhibitors with novel modes of action.     

Hierarchical Coherent Phonons in a Superatomic Semiconductor

The coupling of phonons to electrons and other phonons plays a defining role in material properties, such as charge and energy transport, light emission, and superconductivity. In atomic solids, phonons are delocalized over the 3D lattice, in contrast to molecular solids where localized vibrations dominate. Here, a hierarchical semiconductor that expands the phonon space by combining localized 0D modes with delocalized 2D and 3D modes is described. This material consists of superatomic building blocks (Re₆Se₈) covalently linked into 2D sheets that are stacked into a layered van der Waals lattice. Using transient reflectance spectroscopy, three types of coherent phonons are identified: localized 0D breathing modes of isolated superatom, 2D synchronized twisting of superatoms in layers, and 3D acoustic interlayer deformation. These phonons are coupled to the electronic degrees of freedom to varying extents. The presence of local phonon modes in an extended crystal opens the door to controlling material properties from hierarchical phonon engineering.     

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient''s disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [¹⁸F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.     

Dichlorinated Dithienylethene-Based Copolymers for Air-Stable n-Type Conductivity and Thermoelectricity

Two donor–acceptor (D–A) polymers are obtained by coupling difluoro- and dichloro-substituted forms of the electron-deficient unit BDOPV and the relatively weak donor moiety dichlorodithienylethene (ClTVT). The conductivity and power factors of doped devices are different for the chlorinated and fluorinated BDOPV polymers. A high electron conductivity of 38.3 and 16.1 S cm⁻¹ are obtained from the chlorinated and fluorinated polymers with N-DMBI, respectively, and 12.4 and 2.4 S cm⁻¹ are obtained from the chlorinated and fluorinated polymers with CoCp₂, respectively, from drop-cast devices. The corresponding power factors are 22.7, 7.6, 39.5, and 8.0  µ W m⁻¹ K⁻², respectively. Doping of PClClTVT with N-DMBI results in excellent air stability; the electron conductivity of devices with 50 mol% N-DMBI as dopant remained up to 4.9 S m⁻¹ after 222 days in the air, the longest for an n-doped polymer stored in air, with a thermoelectric power factor of 9.3  µ W m⁻¹ K⁻². However, the conductivity of PFClTVT-based devices can hardly be measured after 103 days. These observations are consistent with morphologies determined by grazing incidence wide angle X-ray scattering and atomic force microscopy.     

An augmented EM algorithm for monotonic Bayesian networks using parameterized conditional probability tables

Bayesian networks offer an attractive framework for describing the relationship between latent proficiency variables and observable outcomes. In educational applications, it is useful to restrict the conditional probability tables of the Bayesian network to be monotonic—increasing skill implies a high chance of a good performance. This paper describes the DiBello family of models for Bayesian networks, which enforce monotonicity, and introduces an augmented EM algorithm for estimating the parameters of these models. In a calibration experiment using simulated data, the algorithm did a good job recovering the model parameters and the conditional probability tables with sample sizes as low as 400.

     

Subtle variations in the structure of crosslinked epoxy networks and the impact upon mechanical and thermal properties

Presented here is an investigation of the structure–property relationships of crosslinked networks using three bi-functional glycidyl ether aromatic epoxy resins, two bi-aryl and one tri-aryl, cured with bi- and tri-aryl amines. Subtle changes to the monomer chemistry including changing aromatic substitution patterns from meta to para, methylene to isopropyl and isopropyl to ether were explored. Changing an epoxy resin backbone from methylene to isopropyl enhances backbone rigidity thus increasing glass transition temperature (T_g), yield strength, and strain despite reducing modulus. Changing meta-substitution to para increases T_g and yield strain while leaving strength unaffected and reducing modulus. Changing isopropyl linkages to ether reduces modulus, strength, T_g, and yield strain reflecting increased molecular flexibility. Using three instead of two aromatic rings increases the molecular weight between crosslinks thereby decreasing T_g and yield strain while increasing modulus and strength. Despite the complexities of multiple systems for varying epoxy resins and amine hardeners, the effect upon network properties is explained in terms of short- and long-range molecular and segmental mobility, crosslink density, and equilibrium packing density. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48874.