A Multiplexing Activity-Based Protein-Profiling Platform for Dissection of a Native Bacterial Xyloglucan-Degrading System.

Bacteria and yeasts grow on biomass polysaccharides by expressing and excreting a complex array of glycoside hydrolase (GH) enzymes. Identification and annotation of such GH pools, which are valuable commodities for sustainable energy and chemistries, by conventional means (genomics, proteomics) are complicated, as primary sequence or secondary structure alignment with known active enzymes is not always predictive for new ones. Here we report a "low-tech", easy-to-use, and sensitive multiplexing activity-based protein-profiling platform to characterize the xyloglucan-degrading GH system excreted by the soil saprophyte, , when grown on xyloglucan.

β-l-furano-cyclitol Aziridines Are Covalent Broad-Spectrum Inhibitors and Activity-Based Probes for Retaining β-l-Arabinofuranosidases.

GH127 and GH146 microorganismal retaining β-l-arabinofuranosidases, expressed by human gut microbiomes, feature an atypical catalytic domain and an unusual mechanism of action. We recently reported that both GH146 and HypBA1 are inhibited by β-l-furanosyl cyclophellitol epoxide, supporting the action of a zinc-coordinated cysteine as a catalytic nucleophile, where in most retaining GH families, an aspartate or glutamate is employed. This work presents a panel of β-l-furanosyl cyclophellitol epoxides and aziridines as mechanism-based GH146/HypBA1 inhibitors and activity-based probes.

The development of a broad-spectrum retaining β-exo-galactosidase activity-based probe.

Acid β-galactosidase (GLB1) and galactocerebrosidase (GALC) are retaining exo-β-galactosidases involved in lysosomal glycoconjugate metabolism. Deficiency of GLB1 may result in the lysosomal storage disorders GM1 gangliosidosis, Morquio B syndrome, and galactosialidosis, and deficiency of GALC may result in Krabbe disease. Activity-based protein profiling (ABPP) is a powerful technique to assess the activity of retaining glycosidases in relation to health and disease.

Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning.

In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni-type procedure to control Type I errors in the strong sense. Given Phase II study results or other prior knowledge, it is usually of main interest to find the optimal graph that maximizes a certain objective function in a future Phase III study. In this article, we evaluate the performance of two existing derivative-free constrained methods, and further propose a deep learning enhanced optimization framework.