Kageyama, M. | bioRxiv Science

bioRxiv2024

A beta-Galactosidase acting on unique galactosides: the structure and function of a beta-1,2-galactosidase from Bacteroides xylanisolvens, an intestinal bacterium

Galactosides are major carbohydrates that are found in plant cell walls and various prebiotic oligosaccharides. Studying the detailed biochemical functions of {beta}-galactosidases in degrading these carbohydrates is important. In particular, identifying {beta}-galactosidases with new substrate specificities could help in the production of potentially beneficial oligosaccharides. In this study, we identified a {beta}-galactosidase with novel substrate specificity from Bacteroides xylanisolvens, an intestinal bacterium. The enzyme did not show hydrolytic activity toward natural {beta}-galactosides during the first screening. However, when -D-galactosyl fluoride (-GalF) as a donor substrate and galactose or D-fucose as an acceptor substrate were incubated with a nucleophile mutant, reaction products were detected. The galactobiose produced from the -GalF and galactose was identified as {beta}-1,2-galactobiose using NMR. Kinetic analysis revealed that this enzyme effectively hydrolyzed {beta}-1,2-galactobiose and {beta}-1,2-galactotriose. In the complex structure with methyl {beta}-galactopyranose as a ligand, the ligand is only located at subsite +1. The 2-hydroxy group and the anomeric methyl group of methyl {beta}-galactopyranose faces in the direction of subsite -1 and the solvent, respectively. This observation is consistent with the substrate specificity of the enzyme regarding linkage position and chain length. Overall, we concluded that the enzyme is a {beta}-galactosidase acting on {beta}-1,2-galactooligosaccharides. SynopsisThe structural and functional analysis of {beta}-galactosidase from an intestinal bacterium led to the discovery of a new {beta}-galactosidase hydrolyzing unique {beta}-1,2-galactooligosaccharides.

biochemistry↗

bioRxiv2024

Development of a novel in silico classification model to assess reactive metabolite formation in the cysteine trapping assay and investigation of important substructures

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of its mechanism. The production of reactive metabolites is one of the major causes of DILI, particularly idiosyncratic DILI. The cysteine trapping assay is one of the methods to detect reactive metabolites which bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay to accelerate the drug discovery process. In this study, we collected 475 compounds (436 in-house compounds and 39 publicly available drugs). Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. The 5-fold cross-validation (CV) and hold-out test were evaluated in random- and time-split trials. Additionally, we investigated the substructures that contributed to positive results in the cysteine trapping assay through the framework of the MPNN model. In the random-split dataset, the AUC-ROC of MPNN and RF were 0.698 and 0.811 in the 5-fold CV, and 0.742 and 0.819 in the hold-out test, respectively. In the time-split dataset, AUC-ROC of MPNN and RF were 0.729 and 0.617 in the 5-fold CV, and 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay have better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous reports such as propranolol, verapamil, and imipramine. This is a new machine learning model that can determine the outcome of the cysteine trapping assay, namely accurately predicting the covalent binding risk as the one of factors of idiosyncratic DILI. We believe that this can contribute to mitigating DILI risk for reactive metabolites at the early stages of drug discovery.

bioinformatics↗