Specificity and Kinetics of Haloalkane Dehalogenase

Haloalkane dehalogenase converts halogenated alkanes to their corresponding alcohols. The active site is buried inside the protein and lined with hydrophobic residues. The reaction proceeds via a covalent substrate-enzyme complex. This paper describes a steady-state and pre-steady-state kinetic analysis of the conversion of a number of substrates of the dehalogenase. The kinetic mechanism for the “natural” substrate 1,2-dichloroethane and for the brominated analog and nematocide 1,2-dibromoethane are given. In general, brominated substrates had a lower Km, but a similar kcat than the chlorinated analogs. The rate of C-Br bond cleavage was higher than the rate of C-Cl bond cleavage, which is in agreement with the leaving group abilities of these halogens. The lower Km for brominated compounds therefore originates both from the higher rate of C-Br bond cleavage and from a lower Ks for bromo-compounds. However, the rate-determining step in the conversion (kcat) of 1,2-dibromoethane and 1,2-dichloroethane was found to be release of the charged halide ion out of the active site cavity, explaining the different Km but similar kcat values for these compounds. The study provides a basis for the analysis of rate-determining steps in the hydrolysis of various environmentally important substrates.

Influence of mutations of Val226 on the catalytic rate of haloalkane dehalogenase

Haloalkane dehalogenase converts haloalkanes to their corresponding alcohols. The 3D structure, reaction mechanism and kinetic mechanism have been studied. The steady state kcat with 1,2-dichloroethane and 1,2-dibromoethane is limited mainly by the rate of release of the halide ion from the buried active-site cavity. During catalysis, the halogen that is cleaved off (Clα) from 1,2-dichloroethane interacts with Trp125 and the Clβ interacts with Phe172. Both these residues have van der Waals contacts with Val226. To establish the effect of these interactions on catalysis, and in an attempt to change enzyme activity without directly mutating residues involved in catalysis, we mutated Val226 to Gly, Ala and Leu. The Val226Ala and Val226Leu mutants had a 2.5-fold higher catalytic rate for 1,2-dibromoethane than the wild-type enzyme. A pre-steady state kinetic analysis of the Val226Ala mutant enzyme showed that the increase in kcat could be attributed to an increase in the rate of a conformational change that precedes halide release, causing a faster overall rate of halide dissociation. The kcat for 1,2-dichloroethane conversion was not elevated, although the rate of chloride release was also faster than in the wild-type enzyme. This was caused by a 3-fold decrease in the rate of formation of the alkyl-enzyme intermediate for 1,2-dichloroethane. Val226 seems to contribute to leaving group (Clα or Brα) stabilization via Trp125, and can influence halide release and substrate binding via an interaction with Phe172. These studies indicate that wild-type haloalkane dehalogenase is optimized for 1,2-dichloroethane, although 1,2-dibromoethane is a better substrate.

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Occupational (Im)mobility in the Global Care Economy: The Case of Foreign-Trained Nurses in the Canadian Context

The twenty-first century has witnessed a number of significant demographic and political shifts that have resulted in a care crisis. Addressing the deficit of care provision has led many nations to actively recruit migrant care labour, often under temporary forms of migration. The emergence of this phenomenon has resulted in a rich field of analysis using the lens of care, including the idea of the Global Care Chain. Revisions to this conceptualization have pushed for its extension beyond domestic workers in the home to include skilled workers in other institutional settings, particularly nurses in hospitals and long-term care settings. Reviewing relevant literature on migrant nurses, this article explores the labour market experiences of internationally educated nurses in Canada. The article reviews research on the barriers facing migrant nurses as they transfer their credentials to the Canadian context. Analysis of this literature suggests that internationally trained nurses experience a form of occupational (im)mobility, paradoxical, ambiguous and contingent processes that exploit global mobility, and results in the stratified incorporation of skilled migrant women into healthcare workplaces

Phalangeal fractures of the hand:An analysis of gender and age-related incidence and aetiology

The incidence and aetiology of 6,857 phalangeal fractures of the hand have been reviewed in a series of 235,427 patients, looking for an age-specific vulnerability to fracture. We found sports to be the main cause of fracture in the 10-29 years age groups and accidental falls to be the leading cause in those aged 70 years or older. We made a new observation that the highest incidence occurs in the male 40-69 age group and machinery was the dominant cause of fracture in this group. Recognition of the frequency of industrial trauma is needed, and public expenditure should be invested in its prevention and treatment.</p

Anharmonic effects in the A15 compounds induced by sublattice distortions

We demonstrate that elastic anomalies and lattice instabilities in the the A15 compounds are describable in terms of first-principles LDA electronic structure calculations. We show that at T=0 V_3Si, V_3Ge, and Nb_3Sn are intrinsically unstable against shears with elastic moduli C_11-C_12 and C_44, and that the zone center phonons, Gamma_2 and Gamma_12, are either unstable or extremely soft. We demonstrate that sublattice relaxation (internal strain) effects are key to understanding the behavior of the A15 materials.Comment: 5 pages, RevTex, 3 postscript figures, Submitted to Phys. Rev. Lett. Apr. 23, 1997 July 7, 1997: minor corrections, final accepted versio