Glycosidic linkage conformation of methyl-α-mannopyranoside

From Quantum Mechanics, Classical Mechanics, and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases

The amyloid β-protein is an intrinsically disordered protein that has the potential to assemble into myriad structures, including oligomers and fibrils. These structures are neurotoxic and are thought to initiate a cascade of events leading to Alzheimer's disease. Understanding this pathogenetic process and elucidating targets for drug therapy depends on elucidation of the structural dynamics of Aβ assembly. In this chapter, we describe work packages required to determine the three-dimensional structures of Aβ and of smaller bioactive fragments thereof, which may be important in AD pathogenesis. These packages include density functional theory, Car-Parrinello molecular dynamics simulations, temperature-dependent replica exchange molecular dynamics simulations, disorder predictors based on bioinformatics, and neural network deep learning.

Challenges and limitations in the studies of glycoproteins: A computational chemist's perspective

Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycoproteins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.

Entropic-Based Separation of Diastereomers: Size-Exclusion Chromatography with Online Viscometry and Refractometry Detection for Analysis of Blends of Mannose and Galactose Methyl-α-pyranosides at “Ideal” Size-Exclusion Conditions

The separation of carbohydrate diastereomers by an ideal size-exclusion mechanism, i.e., in the absence of enthalpic contributions to the separation, can be considered one of the grand challenges in chromatography: Can a difference in the location of a single axial hydroxy group on a pyranose ring (e.g., the axial OH being located on carbon 2 versus on carbon 4 of the ring) sufficiently affect the solution conformational entropy of a monosaccharide in a manner which allows for members of a diastereomeric pair to be separated from each other by size-exclusion chromatography (SEC)? Previous attempts at answering this question, for aqueous solutions, have been thwarted by the mutarotation of sugars in water. Here, the matter is addressed by employing the non-mutarotating methyl-α-pyranosides of d-mannose and d-galactose. We show for the first time, using SEC columns, the entropically driven separation of members of this diastereomeric pair, at a resolution of 1.2–1.3 and with only a 0.4–1% change in solute distribution coefficient over a 25 °C range, thereby demonstrating the ideality of the separation. It is also shown how the newest generation of online viscometer allows for improved sensitivity, thereby extending the range of this so-called molar-mass-sensitive detector into the monomeric regime. Detector multidimensionality is showcased via the synergism of online viscometry and refractometry, which combine to measure the intrinsic viscosity and viscometric radius of the sugars continually across the elution profiles of each diastereomer, methyl-α-d-mannopyranoside and methyl-α-d-galactopyranoside.

Quantum Chemistry Meets Deep Learning for Complex Carbohydrate and Glycopeptide Species I

Carbohydrate complexes are crucial in many various biological and medicinal processes. The impacts of N-acetyl on the glycosidic linkage flexibility of methyl β-D-glucopyranose, and of the glycoamino acid β-D-glucopyranose-asparagine are poorly understood at the electronic level. Furthermore, the effect of D- and L-isomers of asparagine in the complexes of N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine is unknown. In this study, we performed density functional theory calculations of methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine for studying their linkage flexibilities, total solvated energies, thermochemical properties and intra-molecular hydrogen bond formations in an aqueous solution environment using the COnductor-like Screening MOdel (COSMO) for water. We linked these density functional theory calculations to deep learning via estimating the total solvated energy of each linkage torsional angle value. Our results show that deep learning methods accurately estimate the total solvated energies of complex carbohydrate and glycopeptide species and provide linkage flexibility trends for methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine in agreement with density functional theory results. To the best of our knowledge, this study represents the first application of density functional theory along with deep learning for complex carbohydrate and glycopeptide species in an aqueous solution medium. In addition, this study shows that a few thousands of optimization frames from DFT calculations are enough for accurate estimations by deep learning tools.