Computational modeling of carbohydrate processing enzymes reactions

Structural analyses of Glycyrrhiza glabra C-glycosyltransferase: a molecular dynamics study to elucidate catalytically active complexes

C-Glycosides belong to a class of bioactive compounds biosynthesized by C-glycosyltransferases, also known as C-GTs. Despite their practical significance, C-GTs have scarcely been studied due to the limited availability of their crystal structures. In this study, we applied molecular dynamics (MD) simulations and density functional theory (DFT) calculations to investigate Glycyrrhiza glabra C-GT (GgCGT), focusing on the impact of protonation states of two histidine residues and specific mutations on enzyme-substrate configurations. We explored nine native ternary models, considering all possible combinations of protonation states for the His351/His373 pair, which we proposed as fundamental for catalysis. We also included four different mutants designed to assess the role of residues found to be essential for catalytic activity through mutagenesis experiments: His12Ala, His12Lys, His12Lysn and Asp375Ala. MD simulations revealed that only two models (M1 and M3) satisfied the criteria for catalytic competence, where the protonation states of His351 and His373 significantly influenced the relative position between donor and acceptor substrates, as well as the acceptor substrate conformation, adopting extended and packed states. DFT calculations confirmed that these conformations impact the electron density distribution, influencing substrate reactivity. Mutant simulations further supported the experimental data: His12Ala, His12Lys, and Asp375Ala mutants failed to meet the catalytic distance criteria, leading us to infer that these mutations prevented the formation of a reactive enzyme-substrate complex. Conversely, the His12Lysn mutant partially meets the criteria, which could help to explain the catalytic activity of this mutant. These findings provide the first molecular interpretation of the role of key residues in substrate binding and catalysis, which are essential for understanding catalytic activity. Furthermore, they offer new structural insights into residues such as His351/His373, which are often overlooked in GT modeling despite their potential to modulate the Michaelis complex. We hope that these findings will contribute to the rational engineering of more efficient C-GTs for biotechnological applications.

The Conformation of Glycosidic Linkages According to Various Force Fields: Monte Carlo Modeling of Polysaccharides Based on Extrapolation of Short-Chain Properties

The conformational features of the glycosidic linkage are the most important variable to consider when studying di-, oligo-, and polysaccharide molecules using molecular dynamics (MD) simulations. The accuracy of the theoretical model describing this degree of freedom influences the quality of the results obtained from MD calculations based on this model. This article focuses on the following two issues related to the conformation of the glycosidic linkage. First, we describe the results of a comparative analysis of the predictions of three carbohydrate-dedicated classical force fields for MD simulations, namely, CHARMM, GLYCAM, and GROMOS, in the context of different parameters of structural and energetic nature related to the conformation of selected types of glycosidic linkages, α(1 → 4), β(1 → 3), and β(1 → 4), connecting glucopyranose units. This analysis revealed several differences, mainly concerning the energy levels of the secondary and tertiary conformers and the linkage flexibility within the dominant exo-syn conformation for α(1 → 4) and β(1 → 3) linkages. Some aspects of the comparative analysis also included the newly developed, carbohydrate-dedicated Martini 3 coarse-grained force field. Second, to overcome the time-scale problem associated with sampling slow degrees of freedom in polysaccharide chains during MD simulations, we developed a coarse-grained (CG) model based on the data from MD simulations and designed for Monte Carlo modeling. This model (CG MC) is based on information from simulations of short saccharide chains, effectively sampled in atomistic MD simulations, and is capable of extrapolating local conformational properties to the case of polysaccharides of arbitrary length. The CG MC model has the potential to estimate the conformations of very long polysaccharide chains, taking into account the influence of secondary and tertiary conformations of glycosidic linkages. With respect to the comparative analysis of force fields, the application of CG MC modeling showed that relatively small differences in the predictions of individual force fields with respect to a single glycosidic linkage accumulate when considering their effect on the structure of longer chains, leading to drastically different predictions with respect to parameters describing the polymer conformation, such as the persistence length.

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 OleTJE, 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 Cα and Cβ hydroxylation and desaturation. These product distributions were found to be strongly dependent on whether the substrate had substituents on the Cα and/or Cβ atoms. To understand the bifurcation pathways of substrate activation by P450 OleTJE leading to decarboxylation, Cα hydroxylation, Cβ hydroxylation and Cα-Cβ 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 OleTJE guides a reaction to a chemoselectivity.