In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis

With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 Ser/Thr protein kinases of Mtb and is responsible for phosphorylation-mediated signaling, mainly involved in cell wall synthesis, cell division and metabolism. With the amount of structural information available and the great interest in protein kinases, PknB has become an attractive target for drug development. This work describes the optimization and application of an in silico computational protocol to find new PknB inhibitors. This multi-level computational approach combines protein–ligand docking, structure-based virtual screening, molecular dynamics simulations and free energy calculations. The optimized protocol was applied to screen a large dataset containing 129,650 molecules, obtained from the ZINC/FDA-Approved database, Mu.Ta.Lig Virtual Chemotheca and Chimiothèque Nationale. It was observed that the most promising compounds selected occupy the adenine-binding pocket in PknB, and the main interacting residues are Leu17, Val26, Tyr94 and Met155. Only one of the compounds was able to move the active site residues into an open conformation. It was also observed that the P-loop and magnesium position loops change according to the characteristics of the ligand. This protocol led to the identification of six compounds for further experimental testing while also providing additional structural information for the design of more specific and more effective derivatives.     

Sampling CASE Application for the Quality Control of Published Natural Product Structures

Structure elucidation with NMR correlation data is dicey, as there is no way to tell how ambiguous the data set is and how reliably it will define a constitution. Many different software tools for computer assisted structure elucidation (CASE) have become available over the past decades, all of which could ensure a better quality of the elucidation process, but their use is still not common. Since 2011, WebCocon has integrated the possibility to generate theoretical NMR correlation data, starting from an existing structural proposal, allowing this theoretical data then to be used for CASE. Now, WebCocon can also read the recently presented NMReDATA format, allowing for uncomplicated access to CASE with experimental data. With these capabilities, WebCocon presents itself as an easily accessible Web-Tool for the quality control of proposed new natural products. Results of this application to several molecules from literature are shown and demonstrate how CASE can contribute to improve the reliability of Structure elucidation with NMR correlation data.     

Lexicographic optimization for the multi-container loading problem with open dimensions for a shoe manufacturer

4OR - Motivated by a real-world application, we present a multi-container loading problem with 3-open dimensions. We formulate it as a biobjective mixed-integer nonlinear program with lexicographic...     

Acid-Resistant BODIPY Amino Acids for Peptide-Based Fluorescence Imaging of GPR54 Receptors in Pancreatic Islets

The G protein-coupled kisspeptin receptor (GPR54 or KISS1R) is an important mediator in reproduction, metabolism and cancer biology; however, there are limited fluorescent probes or antibodies for direct imaging of these receptors in cells and intact tissues, which can help to interrogate their multiple biological roles. Herein, we describe the rational design and characterization of a new acid-resistant BODIPY-based amino acid (Trp-BODIPY PLUS), and its implementation for solid-phase synthesis of fluorescent bioactive peptides. Trp-BODIPY PLUS retains the binding capabilities of both short linear and cyclic peptides and displays notable turn-on fluorescence emission upon target binding for wash-free imaging. Finally, we employed Trp-BODIPY PLUS to prepare some of the first fluorogenic kisspeptin-based probes and visualized the expression and localization of GPR54 receptors in human cells and in whole mouse pancreatic islets by fluorescence imaging.     

β-Cyclodextrin PAMAM Dendrimer Surface Doped with Silver and Hexacyanoferrate (III) and its Applications for Dopamine Detection in Synthetic Samples

A synthesis of β-cyclodextrin (β-Cd) functionalized with polyamide dendrimer PAMAM G.0 complexed with silver and potassium hexacyanoferrate (III) forming a mixed-valence complex (β-Cd-PAMAM-Ag/Fe) was proposed. This material was characterized by scanning electron microscopy (SEM), infrared spectroscopy (FT-IR), X-Ray diffraction (XRD), Energy Dispersion Spectroscopy (EDS) and Electron Paramagnetic Resonance (EPR). The hybrid complex was electrochemically investigated using cyclic voltammetry and some parameters (electrolyte, concentration of electrolyte, pH and scan rate) were evaluate in order to obtain optimum analytical responses. The β-Cd-PAMAM-Ag/Fe material was successfully applied in the electrocatalytic oxidation of dopamine (DOP) using Cyclic Voltammetry (CV) and Square Wave Voltammetry (SWV).     

Fluorogenic Granzyme A Substrates Enable Real-Time Imaging of Adaptive Immune Cell Activity

Cytotoxic immune cells, including T lymphocytes (CTLs) and natural killer (NK) cells, are essential components of the host response against tumors. CTLs and NK cells secrete granzyme A (GzmA) upon recognition of cancer cells; however, there are very few tools that can detect physiological levels of active GzmA with high spatiotemporal resolution. Herein, we report the rational design of the near-infrared fluorogenic substrates for human GzmA and mouse GzmA. These activity-based probes display very high catalytic efficiency and selectivity over other granzymes, as shown in tissue lysates from wild-type and GzmA knock-out mice. Furthermore, we demonstrate that the probes can image how adaptive immune cells respond to antigen-driven recognition of cancer cells in real time.     

Bottom-Up Preparation of Phase-Separated Polymersomes

A bottom-up approach to fabricating monodisperse, two-component polymersomes that possess phase-separated (“patchy”) chemical topology is presented. This approach is compared with already-existing top-down preparation methods for patchy polymer vesicles, such as film rehydration. These findings demonstrate a bottom-up, solvent-switch self-assembly approach that produces a high yield of nanoparticles of the target size, morphology, and surface topology for drug delivery applications, in this case patchy polymersomes of a diameter of ≈50 nm. In addition, an image processing algorithm to automatically calculate polymersome size distributions from transmission electron microscope images based on a series of pre-processing steps, image segmentation, and round object identification is presented.