Unimolecular, Bimolecular, and Intramolecular Hydrolysis Mechanisms of 4-Nitrophenyl β-d-Glucopyranoside

A novel "Turn-on" fluorometric assays triggered reaction for β-glucosidase activity based on quercetin derived silicon nanoparticles and its potential use for cell imaging

β-Glucosidase (β-Gluco) is an enzyme that is crucial to numerous diseases, including cancer, and in sector of industries, it is used in the manufacturing of food. Measuring its enzymatic activity is critical for biomedical studies and other activities. Herein, we have developed a novel and precise fluorescent sensing method for measuring β-Gluco activity based on the production of yellow-green fluorescent quercetin-silicon nanoparticles (Q-SiNPs) produced from quercetin (QN) as a reducing agent and 3-[2-(2-aminoethyl amino) ethylamino] propyl-trimethoxy silane (AEEA) as a silane molecule. β-Gluco hydrolyzed quercetin-3-O-β-d-glucopyranoside (QO-β-DG) to produce QN, which was then used to produce Q-SiNPs. Reaction parameters, including temperature, time, buffer, pH, and probe concentration, were carefully tuned in this study. Subsequently, the fluorescence intensity was performed, showing good linearity (R2 = 0.989), a broad linear dynamic range between 0.5 and 12 U L-1, and a limit of detection (LOD) as low as 0.428 U L-1, which was proven by fluorescence measurements. Most importantly, various parameters were detected and characterized with or without β-Gluco. The designed probe was successively used to assess β-Gluco activity in human serum and moldy bread. However, the mathematical findings revealed recoveries for human serum ranging from 99.3 to 101.66% and for moldy bread from 100.11 to 102.5%. Additionally, Q-SiNPs were well suited to being incubated in vitro with L929 and SiHa living cells, and after using an Olympus microscope, imaging showed good fluorescence cell images, and their viability evinced minimal cytotoxicity of 77% for L929 and 88% for SiHa. The developed fluorescence biosensor showed promise for general use in diagnostic tests. Therefore, due to this outstanding sensing modality, we anticipate that this research can provide a novel schematic project for creating simple nanostructures with a suitable plan and a green synthetic option for enzyme activity and cell imaging.

A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants

Pesticides, pharmaceuticals, and other organic contaminants often undergo hydrolysis when released into the environment; therefore, measured or estimated hydrolysis rates are needed to assess their environmental persistence. An intuitive multiple linear regression (MLR) approach was used to develop robust QSARs for predicting base-catalyzed rate constants of carboxylic acid esters (CAEs) and lactones. We explored various combinations of independent descriptors, resulting in four primary models (two for lactones and two for CAEs), with a total of 15 and 11 parameters included in the CAE and lactone QSAR models, respectively. The most significant descriptors include pKa, electronegativity, charge density, and steric parameters. Model performance is assessed using Drug Theoretics and Cheminformatics Laboratory's DTC-QSAR tool, demonstrating high accuracy for both internal validation (r2 = 0.93 and RMSE = 0.41-0.43 for CAEs; r2 = 0.90-0.93 and RMSE = 0.38-0.46 for lactones) and external validation (r2 = 0.93 and RMSE = 0.43-0.45 for CAEs; r2 = 0.94-0.98 and RMSE = 0.33-0.41 for lactones). The developed models require only low-cost computational resources and have substantially improved performance compared to existing hydrolysis rate prediction models (HYDROWIN and SPARC).

Identification of fungal lignocellulose-degrading biocatalysts secreted by Phanerochaete chrysosporium via activity-based protein profiling

Activity-based protein profiling (ABPP) has emerged as a versatile biochemical method for studying enzyme activity under various physiological conditions, with applications so far mainly in biomedicine. Here, we show the potential of ABPP in the discovery of biocatalysts from the thermophilic and lignocellulose-degrading white rot fungus Phanerochaete chrysosporium. By employing a comparative ABPP-based functional screen, including a direct profiling of wood substrate-bound enzymes, we identify those lignocellulose-degrading carbohydrate esterase (CE1 and CE15) and glycoside hydrolase (GH3, GH5, GH16, GH17, GH18, GH25, GH30, GH74 and GH79) enzymes specifically active in presence of the substrate. As expression of fungal enzymes remains challenging, our ABPP-mediated approach represents a preselection procedure for focusing experimental efforts on the most promising biocatalysts. Furthermore, this approach may also allow the functional annotation of domains-of-unknown functions (DUFs). The ABPP-based biocatalyst screening described here may thus allow the identification of active enzymes in a process of interest and the elucidation of novel biocatalysts that share no sequence similarity to known counterparts.