This stands in contrast with the subtilisin BPN’ that has no PA d

This stands in contrast with the subtilisin BPN’ that has no PA domain, and where the enzyme makes stronger interaction with residues on the amino side of the cleaved bond. The variable patterns of interactions between the substrate models and PA domains of tomato SBT3 and soybean protease C1 illustrate a crucial role for the PA domain in molecular recognition of their substrates. (C) 2012

CP-456773 Elsevier Masson SAS. All rights reserved.”
“Hafnia-alumina nanolaminates show improved smoothness and reduced crystallinity relative to pure hafnia in films formed by atomic layer deposition (ALD). However, typical nanolaminates also show reduced cross-plane thermal conductivity due to the much larger interface density relative to continuous films. We find that the interface thermal resistance in hafnia-alumina nanolaminates is very low and does not dominate the film thermal conductivity, which is 1.0 to 1.2 W/(m K) at room temperature in 100 nm thin

films regardless of the interface density. Measured films had a number of interfaces ranging from 2 to 40, equivalent to interface spacing varying from about 40 to 2 nm. The degree of crystallinity of these films appears to have a much larger effect on thermal conductivity than that of interface density. Cryogenic measurements show good agreement with both the minimum thermal conductivity model for disordered solids and the diffuse mismatch model of interface resistance down to about 80 check details K before diverging. We find that the films are quite smooth through a 400:5 ratio of hafnia to alumina in terms of ALD cycles, and the

refractive index scales as expected with increasing alumina concentration. (C) 2011 American Institute of Physics. [doi:10.1063/1.3626462]“
“Comparison of protein structures is important for revealing the evolutionary relationship among proteins, predicting protein functions and predicting protein Stem Cells & Wnt inhibitor structures. Many methods have been developed in the past to align two or multiple protein structures. Despite the importance of this problem, rigorous mathematical or statistical frameworks have seldom been pursued for general protein structure comparison. One notable issue in this field is that with many different distances used to measure the similarity between protein structures, none of them are proper distances when protein structures of different sequences are compared. Statistical approaches based on those non-proper distances or similarity scores as random variables are thus not mathematically rigorous. In this work, we develop a mathematical framework for protein structure comparison by treating protein structures as three-dimensional curves. Using an elastic Riemannian metric on spaces of curves, geodesic distance, a proper distance on spaces of curves, can be computed for any two protein structures.

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