Eman Ghanem

Stereoselective Hydrolysis of Organophosphate Nerve Agents by the Bacterial Phosphotriesterase

Organophosphorus compounds include many synthetic, neurotoxic substances that are commonly used as insecticides. The toxicity of these compounds is due to their ability to inhibit the enzyme acetylcholine esterase. Some of the most toxic organophosphates have been adapted for use as chemical warfare agents; the most well-known are GA, GB, GD, GF, VX, and VR. All of these compounds contain a chiral phosphorus center, with the S(P) enantiomers being significantly more toxic than the R(P) enantiomers. Phosphotriesterase (PTE) is an enzyme capable of detoxifying these agents, but the stereochemical preference of the wild-type enzyme is for the R(P) enantiomers. A series of enantiomerically pure chiral nerve agent analogues containing the relevant phosphoryl centers found in GB, GD, GF, VX, and VR has been developed. Wild-type and mutant forms of PTE have been tested for their ability to hydrolyze this series of compounds. Mutant forms of PTE with significantly enhanced, as well as relaxed or reversed, stereoselectivity have been identified. A number of variants exhibited dramatically improved kinetic constants for the catalytic hydrolysis of the more toxic S(P) enantiomers. Improvements of up to 3 orders of magnitude relative to the value of the wild-type enzyme were observed. Some of these mutants were tested against racemic mixtures of GB and GD. The kinetic constants obtained with the chiral nerve agent analogues accurately predict the improved activity and stereoselectivity against the authentic nerve agents used in this study.

Mechanisms Used for Genomic Proliferation by Thermophilic Group II Introns

Studies of mobile group II introns from a thermophilic cyanobacterium reveal how these introns proliferate within genomes and might explain the origin of introns and retroelements in higher organisms.

Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

Differentiation and Identification of Cachaça Wood Extracts Using Peptide-Based Receptors and Multivariate Data Analysis

It is becoming increasingly important to differentiate complex mixtures, especially in forensics. Cachaça, the most popular alcoholic beverage in Brazil, is made from distilled and fermented sugar cane juice. It contains a mixture of naturally occurring polyphenols known as tannins, whose composition is dictated by the type of wood used to age the beverage. These tannins can be differentiated in an Indicator Displacement Assay (IDA) using peptide-based ternary sensing ensembles. This investigation demonstrates a technique for fingerprinting the identity of the woods used to age cachaças. Unknown cachaça samples were tested against a training set of Brazilian woods in addition to oaks from different countries. Results obtained from the analysis showed that 62.5% of the samples were correctly identified. Furthermore, four samples anonymously added to the pool of unknowns from the training set were identified with 100% accuracy, emphasizing both the promising results obtained from this method of differentiation and the importance of analyzing same-age samples.