Catabolism of the Lipofuscin Cycloretinal by MsP1.

Age-related macular degeneration (AMD) is a debilitating eye disease that tends to affect people over the age of 55. Lipofuscins are autofluorescent, toxic byproducts of the visual cycle thought to contribute toward the progression of the disease. Targeting the accumulation of lipofuscin through catabolism may serve as a method for the early treatment of AMD. Thus, an enzymatic approach capable of degrading lipofuscin, cycloretinal (all-trans retinal dimer), was examined. A peroxidase from the organism Marasmius scorodonius (MsP1) has shown capability of degrading this toxic metabolite into nontoxic byproducts. A catalytic triad within MsP1 (D228, H365, and R388) was identified through multiple-sequence alignment and homology modeling and confirmed by kinetic analysis. MsP1-associated cleavage products were detected by gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), and liquid chromatography-tandem mass spectrometry (LC-MSMS). MsP1 degradation byproducts of cycloretinal show reduced cytotoxicity within cell culture (ARPE-19), demonstrating its potential as a gene therapeutic to alleviate the buildup of lipofuscin within AMD.

Biocatalysis with the milk protein ß-lactoglobulin: promoting retroaldol cleavage of α,ß-unsaturated aldehydes.

Enzymes with a hydrophobic binding site and an active site lysine have been suggested to be promiscuous in their catalytic activity. ß-Lactoglobulin (BLG), the principle whey protein found in milk, possesses a central calyx that binds non-polar molecules. Here, we report that BLG can catalyze the retro-aldol cleavage of α,ß-unsaturated aldehydes making it a naturally occurring protein capable of catalyzing retro-aldol reactions on hydrophobic substrates. Retroaldolase activity was seen to be most effective on substrates with phenyl or naphthyl side-chains. Use of a brominated substrate analogue inhibitor increases the product yield by a factor of three. BLG's catalytic activity and its ready availability make it a prime candidate for the development of commercial biocatalysts.

Lipofuscin Formation Catalyzed by the Milk Protein ß-Lactoglobulin: Lysine Residues in Cycloretinal Synthesis.

Lipofuscins are toxic autofluorescent byproducts of the visual cycle. The accumulation of lipofuscins such as cycloretinal in the retina is thought to play a role in the progression of age-related macular degeneration (AMD). Intriguingly, the milk protein ß-lactoglobulin (BLG) can promote the cyclodimerization of all-trans-retinal to cycloretinal both in vitro and in vivo. Here, site-directed mutagenesis of BLG and mass spectrometric analysis with substrate analogues demonstrate that lysine residues play a key role in catalysis. It is also shown that catalytic activity necessitates the presence of a physical binding site and cannot be mediated by a peptide chain. These studies provide insight into the mechanism of the cyclodimerization process and provide a model system for biocatalysis and biosynthesis of cycloretinal in vivo. In the long term, these studies may pave the way for drug development and inhibitor design as an early treatment regimen for AMD.

Priming of Azabicycle Biosynthesis in the Azinomycin Class of Antitumor Agents.

The biosynthesis of the azabicyclic ring system of the azinomycin family of antitumor agents represents the "crown jewel" of the pathway and is a complex process involving at least 14 enzymatic steps. This study reports on the first biosynthetic step, the inroads, in the construction of the novel aziridino [1,2-a]pyrrolidine, azabicyclic core, allowing us to support a new mechanism for azabicycle formation.

Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding.

Foreground segmentation algorithm aims to precisely separate moving objects from the background in various environments. However, the interference from darkness, dynamic background information, and camera jitter makes it still challenging to build a decent detection network. To solve these issues, a triplet CNN and Transposed Convolutional Neural Network (TCNN) are created by attaching a Features Pooling Module (FPM). TCNN process reduces the amount of multi-scale inputs to the network by fusing features into the Foreground Segmentation Network (FgSegNet) based FPM, which extracts multi-scale features from images and builds a strong feature pooling. Additionally, the up-sampling network is added to the proposed technique, which is used to up-sample the abstract image representation, so that its spatial dimensions match with the input image. The large context and long-range dependencies among pixels are acquired by TCNN and segmentation mask, in multiple scales using triplet CNN, to enhance the foreground segmentation of FgSegNet. The results, clearly show that FgSegNet surpasses other state-of-the-art algorithms on the CDnet2014 datasets, with an average F-Measure of 0.9804, precision of 0.9801, PWC as (0.0461), and recall as (0.9896). Moreover, the FgSegNet with up-sampling achieves the F-measure of 0.9804 which is higher when compared to the FgSegNet without up-sampling.