Probing the roles of two tryptophans surrounding the unique zinc coordination site in lipase family I.5

A unique zinc domain found in all of the identified members of the lipase family I.5 is surrounded by two conserved tryptophans (W61 and W212). In this study, we investigated the role of these hydrophobic residues in thermostability and thermoactivity of the lipase from Bacillus thermocatenulatus (BTL2) taken as the representative of the family. Circular dichroism spectroscopy revealed that the secondary structure of BTL2 is conserved by the tryptophan mutations (W61A, W212A, and W61A/W212A), and that W61 is located in a more rigid and less solvent exposed region than is W212. Thermal denaturation and optimal activity analyses pointed out that zinc induces thermostability and thermoactivity of BTL2, in which both tryptophans W61 and W212 play contributing roles. Molecular explanations describing the roles of these tryptophans were pursued by X‐ray crystallography of the open form of the W61A mutant and molecular dynamics simulations which highlighted a critical function for W212 in zinc binding to the coordination site. This study reflects the potential use of hydrophobic amino acids in vicinity of metal coordination sites in lipase biocatalysts design. Proteins 2016; 84:129–142. © 2015 Wiley Periodicals, Inc.     

For what purpose the arts? An analysis of the mission statements of urban arts high schools in Canada and the United States

While general arts programs have declined in many schools across the United States and Canada, the number of specialized art programs in public secondary schools has swelled since the 1980s. While this increase is often celebrated by arts educators, questions about the justification of specialized arts programs are rarely raised, and their value is often taken for granted. In this article, we examine the mission statements of eighty-four specialized arts programs across two countries to examine the ideas, values, and commitments that are expressed in these public statements. In addition to a close thematic analysis, we describe how these mission statements reflect different conceptions of the role of the arts in education and consider the ways in which arguments that seek to broaden access to the arts are combined with the goal of serving a narrow subset of the student population. We argue that analyzing mission statements provides a clearer picture of the ideas that shape these programs, and that in order to foster an informed public conversation about the purpose and value of an education in the arts, educators committed to the arts must engage in this serious discussion.     

Augmented Reticular Thalamic Bursting and Seizures in Scn1a-Dravet Syndrome

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Mafb and c-Maf Have Prenatal Compensatory and Postnatal Antagonistic Roles in Cortical Interneuron Fate and Function

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Trio Haploinsufficiency Causes Neurodevelopmental Disease-Associated Deficits

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Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.