Computational modeling of carbohydrate processing enzymes reactions

Structure and function characterization of the α-L-arabinofuranosidase from the white-rot fungus Trametes hirsuta

α-L-Arabinofuranosidases (Abfs) play a crucial role in the degradation of hemicelluloses, especially arabinoxylans (AX). Most of the available characterized Abfs are from bacteria, while fungi, as natural decomposers, contain Abfs with little attention given. An arabinofuranosidase (ThAbf1), belonging to the glycoside hydrolase 51 (GH51) family, from the genome of the white-rot fungus Trametes hirsuta, was recombinantly expressed, characterized, and functionally determined. The general biochemical properties showed that the optimal conditions for ThAbf1 were pH 6.0 and 50°C. In substrate kinetics assays, ThAbf1 preferred small fragment arabinoxylo-oligosaccharides (AXOS) and could surprisingly hydrolyze di-substituted 2³,3³-di-L-arabinofuranosyl-xylotriose (A^2,3XX). It also synergized with commercial xylanase (XYL) and increased the saccharification efficiency of arabinoxylan. The crystal structure of ThAbf1 indicated the presence of an adjacent cavity next to the catalytic pocket which led to the ability of ThAbf1 to degrade di-substituted AXOS. The narrow binding pocket prevents ThAbf1 from binding larger substrates. These findings have strengthened our understanding of the catalytic mechanism of GH51 family Abfs and provided a theoretical foundation for the development of more efficient and versatile Abfs to accelerate the degradation and biotransformation of hemicellulose in biomass. KEY POINTS: • ThAbf1 from Trametes hirsuta degraded di-substituted arabinoxylo-oligosaccharide. • ThAbf1 performed detailed biochemical characterization and kinetics. • ThAbf1 structure has been obtained to illustrate the substrate specificity.

In silico modelling of the function of disease-related CAZymes

In silico modelling of proteins comprises a diversity of computational tools aimed to obtain structural, electronic, and/or dynamic information about these biomolecules, capturing mechanistic details that are challenging to experimental approaches, such as elusive enzyme-substrate complexes, short-lived intermediates, and reaction transition states (TS). The present article gives the reader insight on the use of in silico modelling techniques to understand complex catalytic reaction mechanisms of carbohydrate-active enzymes (CAZymes), along with the underlying theory and concepts that are important in this field. We start by introducing the significance of carbohydrates in nature and the enzymes that process them, CAZymes, highlighting the conformational flexibility of their carbohydrate substrates. Three commonly used in silico methods (classical molecular dynamics (MD), hybrid quantum mechanics/molecular mechanics (QM/MM), and enhanced sampling techniques) are described for nonexpert readers. Finally, we provide three examples of the application of these methods to unravel the catalytic mechanisms of three disease-related CAZymes: β-galactocerebrosidase (GALC), responsible for Krabbe disease; α-mannoside β-1,6-N-acetylglucosaminyltransferase V (MGAT5), involved in cancer; and O-fucosyltransferase 1 (POFUT1), involved in several human diseases such as leukemia and the Dowling-Degos disease.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Molecular simulations of complex carbohydrates and glycoconjugates

Complex carbohydrates (glycans) are the most abundant and versatile biopolymers in nature. The broad diversity of biochemical functions that carbohydrates cover is a direct consequence of the variety of 3D architectures they can adopt, displaying branched or linear arrangements, widely ranging in sizes, and with the highest diversity of building blocks of any other natural biopolymer. Despite this unparalleled complexity, a common denominator can be found in the glycans' inherent flexibility, which hinders experimental characterization, but that can be addressed by high-performance computing (HPC)-based molecular simulations. In this short review, I present and discuss the state-of-the-art of molecular simulations of complex carbohydrates and glycoconjugates, highlighting methodological strengths and weaknesses, important insights through emblematic case studies, and suggesting perspectives for future developments.

Product Distributions of Cytochrome P450 OleTJE with Phenyl-Substituted Fatty Acids: A Computational Study

There are two types of cytochrome P450 enzymes in nature, namely, the monooxygenases and the peroxygenases. Both enzyme classes participate in substrate biodegradation or biosynthesis reactions in nature, but the P450 monooxygenases use dioxygen, while the peroxygenases take H₂O₂ in their catalytic cycle instead. By contrast to the P450 monooxygenases, the P450 peroxygenases do not require an external redox partner to deliver electrons during the catalytic cycle, and also no external proton source is needed. Therefore, they are fully self-sufficient, which affords them opportunities in biotechnological applications. One specific P450 peroxygenase, namely, P450 OleT_JE, reacts with long-chain linear fatty acids through oxidative decarboxylation to form hydrocarbons and, as such, has been implicated as a suitable source for the biosynthesis of biofuels. Unfortunately, the reactions were shown to produce a considerable amount of side products originating from C^α and C^β hydroxylation and desaturation. These product distributions were found to be strongly dependent on whether the substrate had substituents on the C^α and/or C^β atoms. To understand the bifurcation pathways of substrate activation by P450 OleT_JE leading to decarboxylation, C^α hydroxylation, C^β hydroxylation and C^α–C^β desaturation, we performed a computational study using 3-phenylpropionate and 2-phenylbutyrate as substrates. We set up large cluster models containing the heme, the substrate and the key features of the substrate binding pocket and calculated (using density functional theory) the pathways leading to the four possible products. This work predicts that the two substrates will react with different reaction rates due to accessibility differences of the substrates to the active oxidant, and, as a consequence, these two substrates will also generate different products. This work explains how the substrate binding pocket of P450 OleT_JE guides a reaction to a chemoselectivity.