1,918 research outputs found
Concurrent Cross Metathesis and Enzymatic Oxidation: Enabling Off-Equilibrium Transformations
Crissâcross catalysis: H.â
Zhao, J.â
F.â
Hartwig and coâworkers have combined homogeneous alkene metathesis and biocatalysis in a concurrent fashion. A rutheniumâNâheterocyclic carbene (NHC) complex provides an equilibrating mixture of cross metathesis products. The selective simultaneous epoxidation by cytochrome P450 BM3 enables product yields well above the hypothetical twoâstep process
On the nature of amorphous polymorphism of water
We report elastic and inelastic neutron scattering experiments on different
amorphous ice modifications. It is shown that an amorphous structure (HDA')
indiscernible from the high-density phase (HDA), obtained by compression of
crystalline ice, can be formed from the very high-density phase (vHDA) as an
intermediate stage of the transition of vHDA into its low-density modification
(LDA'). Both, HDA and HDA' exhibit comparable small angle scattering signals
characterizing them as structures heterogeneous on a length scale of a few
nano-meters. The homogeneous structures are the initial and final transition
stages vHDA and LDA', respectively. Despite, their apparent structural identity
on a local scale HDA and HDA' differ in their transition kinetics explored by
in situ experiments. The activation energy of the vHDA-to-LDA' transition is at
least 20 kJ/mol higher than the activation energy of the HDA-to-LDA transition
3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture
Deep learning based computed tomography (CT) reconstruction has demonstrated
outstanding performance on simulated 2D low-dose CT data. This applies in
particular to domain adapted neural networks, which incorporate a handcrafted
physics model for CT imaging. Empirical evidence shows that employing such
architectures reduces the demand for training data and improves upon
generalisation. However, their training requires large computational resources
that quickly become prohibitive in 3D helical CT, which is the most common
acquisition geometry used for medical imaging. Furthermore, clinical data also
comes with other challenges not accounted for in simulations, like errors in
flux measurement, resolution mismatch and, most importantly, the absence of the
real ground truth. The necessity to have a computationally feasible training
combined with the need to address these issues has made it difficult to
evaluate deep learning based reconstruction on clinical 3D helical CT. This
paper modifies a domain adapted neural network architecture, the Learned
Primal-Dual (LPD), so that it can be trained and applied to reconstruction in
this setting. We achieve this by splitting the helical trajectory into sections
and applying the unrolled LPD iterations to those sections sequentially. To the
best of our knowledge, this work is the first to apply an unrolled deep
learning architecture for reconstruction on full-sized clinical data, like
those in the Low dose CT image and projection data set (LDCT). Moreover,
training and testing is done on a single GPU card with 24GB of memory
Capturing conformational states in proteins using sparse paramagnetic NMR data
Capturing conformational changes in proteins or protein-protein complexes is a challenge for both experimentalists and computational biologists. Solution nuclear magnetic resonance (NMR) is unique in that it permits structural studies of proteins under greatly varying conditions, and thus allows us to monitor induced structural changes. Paramagnetic effects are increasingly used to study protein structures as they give ready access to rich structural information of orientation and long-range distance restraints from the NMR signals of backbone amides, and reliable methods have become available to tag proteins with paramagnetic metal ions site-specifically and at multiple sites. In this study, we show how sparse pseudocontact shift (PCS) data can be used to computationally model conformational states in a protein system, by first identifying core structural elements that are not affected by the environmental change, and then computationally completing the remaining structure based on experimental restraints from PCS. The approach is demonstrated on a 27 kDa two-domain NS2B-NS3 protease system of the dengue virus serotype 2, for which distinct closed and open conformational states have been observed in crystal structures. By changing the input PCS data, the observed conformational states in the dengue virus protease are reproduced without modifying the computational procedure. This data driven Rosetta protocol enables identification of conformational states of a protein system, which are otherwise difficult to obtain either experimentally or computationally.This study was supported by the Australian
Research Council (DP120100561, DP150100383),
which the authors gratefully acknowledge
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