Physicochemical Characterization of Polymer-Stabilized Coacervate Protocells

The bottom-up construction of cell mimics has produced a range of membrane-bound protocells that have been endowed with functionality and biochemical processes reminiscent of living systems. The contents of these compartments, however, experience semidilute conditions, whereas macromolecules in the cytosol exist in protein-rich, crowded environments that affect their physicochemical properties, such as diffusion and catalytic activity. Recently, complex coacervates have emerged as attractive protocellular models because their condensed interiors would be expected to mimic this crowding better. Here we explore some relevant physicochemical properties of a recently developed polymer-stabilized coacervate system, such as the diffusion of macromolecules in the condensed coacervate phase, relative to in dilute solutions, the buffering capacity of the core, the molecular organization of the polymer membrane, the permeability characteristics of this membrane towards a wide range of compounds, and the behavior of a simple enzymatic reaction. In addition, either the coacervate charge or the cargo charge is engineered to allow the selective loading of protein cargo into the coacervate protocells. Our in-depth characterization has revealed that these polymer-stabilized coacervate protocells have many desirable properties, thus making them attractive candidates for the investigation of biochemical processes in stable, controlled, tunable, and increasingly cell-like environments.     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.     

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.     

Optimal attacks on qubit-based Quantum Key Recycling

Quantum Key Recycling (QKR) is a quantum cryptographic primitive that allows one to reuse keys in an unconditionally secure way. By removing the need to repeatedly generate new keys, it improves communication efficiency. ?kori? and de Vries recently proposed a QKR scheme based on 8-state encoding (four bases). It does not require quantum computers for encryption/decryption but only single-qubit operations. We provide a missing ingredient in the security analysis of this scheme in the case of noisy channels: accurate upper bounds on the required amount of privacy amplification. We determine optimal attacks against the message and against the key, for 8-state encoding as well as 4-state and 6-state conjugate coding. We provide results in terms of min-entropy loss as well as accessible (Shannon) information. We show that the Shannon entropy analysis for 8-state encoding reduces to the analysis of quantum key distribution, whereas 4-state and 6-state suffer from additional leaks that make them less effective. From the optimal attacks we compute the required amount of privacy amplification and hence the achievable communication rate (useful information per qubit) of qubit-based QKR. Overall, 8-state encoding yields the highest communication rates.     

Renewable linear alpha olefins by selective ethenolysis of decarboxylated unsaturated fatty acids

A two‐step concept for the production of linear alpha olefins from biomass is reported. As a starting material an internally unsaturated C17 alkene was used, which was obtained by the decarboxylation of oleic acid. Here, we report on the ethenolysis of this bio‐based product, using commercially available metathesis catalysts. The desired alpha olefin products, 1‐nonene and 1‐decene, were obtained in excellent yield (96%) and selectivity (96%). Practical applications: The two‐step conversion described in this contribution, starting from unsaturated fatty acids, provides a method for the production of industrially important linear alpha olefins. These valuable products are widely used as starting materials for the production of surfactants and polymers such as linear low density polyethylene (LLDPE).     

Sequential enzymatic quantification of two sugars in a single microchannel

This paper presents the implementation of a multiple analyte enzyme assay, based on the sequential injection of the different enzyme solutions, in an electrokinetic driven microfluidic chip. The assay methodology for the simultaneous quantification of d-glucose and d-fructose was reported in previous publications but here the real integration of both enzyme assays was achieved. When assays were executed separately, good reproducibility was observed with average CV values of 5.2% and 4.5% for the d-glucose and d-fructose assay, respectively. Next, the assays for the quantification of d-glucose and d-fructose were integrated simultaneously on chip, where each assay was executed consecutively in the same microreactor by applying a specific sequence of potentials at the reservoirs. This article proves the integration of a sequential based quantification approach in continuous microfluidic chips with electrokinetic actuation.     

Controlling droplet size variability of a digital lab-on-a-chip for improved bio-assay performance

Although several applications of electrowetting on dielectric digital lab-on-a-chips are reported in literature, there is still a lack of knowledge about the influence of operational and design parameters on the performance of an analytical assay. This paper investigates how droplet size variability, introduced by droplet dispensing and splitting, influences the assay performance with respect to repeatability and accuracy and presents a novel method to reduce this variability. Both a theoretical and experimental approach were followed. Monte Carlo simulations were applied to study the cumulative effect of the variability caused by different droplet manipulations on the final assay performance. It is shown that a highly controllable droplet generation and manipulation is achieved with respect to droplet size variability through an accurate control of actuation voltage, activation time, relaxation time, and electrode size. As a case study, it is illustrated that through optimization of these parameters a complete on-chip calibration curve is obtained for a d-glucose assay with an average CV-value of 2%. These new insights aim to bring the digital lab-on-a-chip technology closer to researchers in the field of diagnostics offering them a valuable and accessible alternative to standard analysis platforms.