[2.2]Paracyclophane-Based TCN-201 Analogs as GluN2A-Selective NMDA Receptor Antagonists

Recent studies have shown the involvement of GluN2A subunit-containing NMDA receptors in various neurological and pathological disorders. In the X-ray crystal structure, TCN-201 ( 1 ) and analogous pyrazine derivatives 2 and 3 adopt a U-shape (hairpin) conformation within the binding site formed by the ligand binding domains of the GluN1 and GluN2A subunits. In order to mimic the resulting π/π-interactions of two aromatic rings in the binding site, a [2.2]paracyclophane system was designed to lock these aromatic rings in a parallel orientation. Acylation of [2.2]paracyclophane ( 5 ) with oxalyl chloride and chloroacetyl chloride and subsequent transformations led to the oxalamide 7 , triazole 10 and benzamides 12 . The GluN2A inhibitory activities of the paracyclophane derivatives were tested with two-electrode voltage clamp electrophysiology using Xenopus laevis oocytes expressing selectively functional NMDA receptors with GluN2A subunit. The o-iodobenzamide 12 b with the highest similarity to TCN-201 showed the highest GuN2A inhibitory activity of this series of compounds. At a concentration of 10 μM, 12 b reached 36 % of the inhibitory activity of TCN-201 ( 1 ). This result indicates that the [2.2]paracyclophane system is well accepted by the TCN-201 binding site.     

Physics inspired algorithms for (co)homology computations of three-dimensional combinatorial manifolds with boundary

The issue of computing (co)homology generators of a cell complex is gaining a pivotal role in various branches of science. While this issue may be rigorously solved in polynomial time, it is still overly demanding for large scale problems. Drawing inspiration from low-frequency electrodynamics, this paper presents a physics inspired algorithm for first cohomology group computations on three-dimensional complexes. The algorithm is general and exhibits orders of magnitude speed up with respect to competing ones, allowing to handle problems not addressable before. In particular, when generators are employed in the physical modeling of magneto-quasistatic problems, this algorithm solves one of the most long-lasting problems in low-frequency computational electromagnetics. In this case, the effectiveness of the algorithm and its ease of implementation may be even improved by introducing the novel concept of lazy cohomology generators.     

Lactic acid production using waste generated from sweet potato processing

Organic waste generated from industrial sweet potato canning is estimated to be 30% of incoming raw material with significant residual carbohydrate content. The purpose of this study was to evaluate the potential of waste generated from sweet potato processing material to support the growth of lactic acid bacteria and the production of lactic acid. The waste was comprised of 16.5% solids consisting of 18.5% ash, 4.4% protein, 20.5% simple sugars and 19% soluble starch. Following a screening of three lactic acid bacteria strains, Lactobacillus rhamnosus was deemed the best candidate for lactic acid production. The potential of various dilutions of the enzyme‐hydrolysed waste, with and without pH control, as a fermentation substrate was evaluated. Lactic acid production was highest in hydrolysed waste (without dilution) at pH set point 5.0, yielding 10 g L^?1 in 72 h. Thus, lactic acid, a valuable organic compound, can be generated from sweet potato waste.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

Effect of Reciprocation Frequency on Friction and Wear of Vibrating Contacts Lubricated with Soybean-Based B100 Biodiesel

The use of renewable, bio-based fuels has become increasingly widespread in recent years, with a major example being biodiesel, a bio-derived alternative to Number 2 diesel fuel. The increased usage of biodiesel gives rise to an augmented need to understand its tribological effects on critical engine components. This study focused on determining the tribological performance of soybean-based B100 (i.e., pure) biodiesel within a fuel injector with varying oscillating frequency by performing a series of linear reciprocating tribological tests of biodiesel-lubricated interfaces with varying reciprocating frequency. Comparison of friction coefficient variation with reciprocating frequency indicated a transition from boundary lubrication to hydrodynamic lubrication as the frequency increased, while hysteresis loop and energy loss observations showed a transition between full stick and partial slip contact with increasing frequency. However, observations of induced wear showed the wear to increase with increasing frequency, most likely due to the augmented number of sliding cycles as well as an increased degree of interfacial slip.     

Microkinetic model for the pyrolysis of methyl esters: From model compound to industrial biodiesel

A tool for the generation of decomposition schemes of large molecules has been developed. These decomposition schemes contain radicals which can be eliminated from the model equations if both the μ‐hypothesis and the pseudosteady‐state approximation are valid. The reaction rate coefficients and thermodynamic parameters have been calculated by incorporating a comprehensive group additive framework. A microkinetic model for the pyrolysis of methyl esters with a carbon number of up to 19 has been generated using this tool. It is validated by comparing calculated and experimental yields of the pyrolysis of methyl decanoate and novel rapeseed methyl ester pyrolysis data in the temperature range from 800 to 1100 K and methyl ester partial pressure range from 1 × 10^?3 to 1 × 10^?2 MPa. This modeling frame work allows to not only assess the use of methyl ester mixtures as potential feedstock for olefin production but also their effect as blend‐in or trace impurity. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4309–4322, 2015