Quantitative Assessment of Affinity Selection Performance by Using DNA-Encoded Chemical Libraries

DNA-encoded chemical libraries are often used for the discovery of ligands against protein targets of interest. These large collections of DNA-barcoded chemical compounds are typically screened by using affinity capture methodologies followed by PCR amplification and DNA sequencing procedures. However, the performance of individual steps in the selection procedures has been scarcely investigated, so far. Herein, the quantitative analysis of selection experiments, by using three ligands with different affinity to carbonic anhydrase IX as model compounds, is described. In the first set of experiments, quantitative PCR (qPCR) procedures are used to evaluate the recovery and selectivity for affinity capture procedures performed on different solid-phase supports, which are commonly used for library screening. In the second step, both qPCR and analysis of DNA sequencing results are used to assess the recovery and selectivity of individual carbonic anhydrase IX ligands in a library, containing 360 000 compounds. Collectively, this study reveals that selection procedures can be efficient for ligands with sub-micromolar dissociation constants to the target protein of interest, but also that selection performance dramatically drops if 10⁴ copies per library member are used as the input.     

Shuffle or update parallel differential evolution for large-scale optimization

This paper proposes a novel algorithm for large-scale optimization problems. The proposed algorithm, namely shuffle or update parallel differential evolution (SOUPDE) is a structured population algorithm characterized by sub-populations employing a Differential evolution logic. The sub-populations quickly exploit some areas of the decision space, thus drastically and quickly reducing the fitness value in the highly multi-variate fitness landscape. New search logics are introduced into the sub-population functioning in order to avoid a diversity loss and thus premature convergence. Two simple mechanisms have been integrated in order to pursue this aim. The first, namely shuffling, consists of randomly rearranging the individuals over the sub-populations. The second consists of updating all the scale factors of the sub-populations. The proposed algorithm has been run on a set of various test problems for five levels of dimensionality and then compared with three popular meta-heuristics. Rigorous statistical and scalability analyses are reported in this article. Numerical results show that the proposed approach significantly outperforms the meta-heuristics considered in the benchmark and has a good performance despite the high dimensionality of the problems. The proposed algorithm balances well between exploitation and exploration and succeeds to have a good performance over the various dimensionality values and test problems present in the benchmark. It succeeds at outperforming the reference algorithms considered in this study. In addition, the scalability analysis proves that with respect to a standard Differential Evolution, the proposed SOUPDE algorithm enhances its performance while the dimensionality grows.     

K- and Cs-FeV/Al2O3soot combustion catalysts for diesel exhaust treatment

Alkali-doped FeV oxide catalysts supported on -alumina were prepared and their catalytic activity in the combustion of diesel soot is reported. The catalysts were characterized by XRD, TPR and SEM–EDX analysis. The influence of the nature of the alkali metal (K and Cs), the temperature of treatment of the catalysts and the stability to sulfur poisoning have been investigated.

Catalysts doped with Cs were the most active and stable also after several combustion cycles and in the presence of sulfur in the stream. The activity measurements and microstructural results suggest that the combustion of soot is favored on catalysts where amorphous phases and/or mixed Fe---V---O phases, ensuring an intimate contact between iron and vanadium, are present. A reaction mechanism involving the participation of the redox couple Fe(II)–Fe(III) in the activation of the vanadium combustion sites, is proposed.     

Packet-mode scheduling in input-queued cell-based switches

We consider input-queued switch architectures dealing at their interfaces with variable-size packets, but internally operating on fixed-size cells. Packets are segmented into cells at input ports, transferred through the switching fabric, and reassembled at output ports. Cell transfers are controlled by a scheduling algorithm, which operates in packet-mode: all cells belonging to the same packet are transferred from inputs to outputs without interruption. We prove that input-queued switches using packet-mode scheduling can achieve 100% throughput, and we show by simulation that, depending on the packet size distribution, packet-mode scheduling may provide advantages over cell-mode scheduling.     

A Novel Calibration Algorithm for a Special Class of Multiport Vector Network Analyzers

A new error model for a special class of multiport vector network analyzers (VNAs) is presented in this paper. This model can be applied to multiport network analyzers with noncomplete reflectometers, i.e., when the measurement of the incident waves at each port is not always available. The method used to compute the error coefficients proposed here is based on a compact and easy formulation. This method is an extension of the already existing general theory for complete reflectometer multiport network analyzers. Furthermore, the new error model generalizes the theory for three-sampler two-port VNAs. The proposed model has been tested against the complete reflectometer one and exhibits the same accuracy level.     

Selection of catalytically active biotin ligase and trypsin mutants by phage display

Phage display has been shown to facilitate greatly the selectionof polypeptides with desired properties by establishing a directlink between the polypeptide and the gene that encodes it. However,selection for catalytic activities displayed on phage remainsa challenge, since reaction products diffuse away from the enzymeand make it difficult to recover catalytically active phage–enzymes.We have recently described a selection methodology in whichthe reaction substrate (and eventually the reaction product)is anchored on calmodulin-tagged phage–enzymes by meansof a calmodulin binding peptide. Phage displaying a catalyticactivity are physically isolated by means of affinity reagentsspecific for the product of reaction. In this study, we investigatedthe efficiency of selection for catalysis by phage display,using a ligase (the Escherichia coli biotin ligase BirA) andan endopeptidase (the rat trypsin His57     

Data mining in a behavioral test detects early symptoms in a model of amyotrophic lateral sclerosis.

"What's wrong with my genetically engineered animal?" is a common yet often difficult to answer question in behavioral phenotyping. We present here a method termed Pattern Array for mining movement patterns and isolating those that best capture an effect of a genetic manipulation. We demonstrate the method by searching for early motor symptoms in the open-field behavior of SOD1 mutant rats, an animal model of amyotrophic lateral sclerosis. Pattern Array was able to identify a unique motor pattern that differentiated the SOD1 mutants from the wild-type controls 2 months before disease onset. This pattern included heavy braking while moving near the arena wall but turning away from it. SOD1 mutants performed this pattern significantly less than wild-type controls in 2 independent data sets. At such early age the SOD1 mutants could not be differentiated from the controls by standard behavioral measures or by subjective observation. The early discovered symptom may enable investigators to test therapies aimed for intervention rather than remediation. Our results demonstrate the feasibility and potential of detecting subtle behavioral effects using data mining strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)