Ligand-Directed Chemistry on Glycoside Hydrolases – A Proof of Concept Study

Selective covalent labelling of enzymes using small molecule probes has advanced the scopes of protein profiling. The covalent bond formation to a specific target is the key step of activity-based protein profiling (ABPP), a method which has become an indispensable tool for measuring enzyme activity in complex matrices. With respect to carbohydrate processing enzymes, strategies for ABPP so far involve labelling the active site of the enzyme, which results in permanent loss of activity. Here, we report in a proof of concept study the use of ligand-directed chemistry (LDC) for labelling glycoside hydrolases near – but not in – the active site. During the labelling process, the competitive inhibitor is cleaved from the probe, departs the active site and the enzyme maintains its catalytic activity. To this end, we designed a building block synthetic concept for small molecule probes containing iminosugar-based reversible inhibitors for labelling of two model β-glucosidases. The results indicate that the LDC approach can be adaptable for covalent proximity labelling of glycoside hydrolases.     

Tetranuclear Thioureacopper(II) Complexes

By replacement of methanol in the Cu₄OX₆(MeOH)₄ (X  Cl, Br) complexes with thiourea (tu), N,N′-diphenylthiourea (diftu), N,N′-dimethlthiourea (dimtu), tetramethylthiourea (tmtu) and acetylthiourea (actu), the complexes Cu₄OCl₆L₄ where L  tu, tu(0.3 Et₂O), diftu, dimtu, tmtu, actu (0.5Et₂O) and the complexes Cu₄OBr₆L₄, where L  tu, diftu, dimtu, tmtu have been prepared. Their infrared and electronic spectra indicate the presence of Cu₄O grouping and the trigonal bipyramidal coordination of copper(II). These complexes exhibit e.p.r. signals in the temperature range 100–260 K, in contrast to other known complexes of this structural type, which showed the e.p.r. signals at significantly lower temperatures. Thiourea and its derivatives are not able to reduce copper(II) in reaction with the tetranuclear Cu₄OX₆(MeOH)₄ complexes in methanol-ether solution due to the redox stabilizing effect of the tetranuclear structure.     

Tool Chain to Extract and Contextualize Process Data for AI Applications

Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high-quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain.