This review will present a summary of carbohydrate 3D structure modeling. 40 Such comparisons highlight the predictive strengths of current modeling methods, and identify areas that require further development. ANOMERIC CARBON SOFTWAREAdvances in computer performance and software algorithms now permit conformational sampling well into the microsecond time scale, permitting convergence of MD simulations, which in turn facilitates quantitative comparisons with experimental data. 20– 24 Indeed, the development and refinement of carbohydrate force fields for AMBER, 25– 29 CHARMm, 14, 30– 34 and GRO-MOS 20, 35– 39 is still ongoing, as weaknesses are discovered and corrected and limitations are removed. But with the recognition of the importance of water and dynamics to oligosaccharide conformations, these methods have largely been replaced in favor of traditional biomolecular force fields. 7– 12 Prior to the widespread adoption of solvated MD simulations for oligosaccharide modeling, much effort was devoted to determining the lowest potential energy (adiabatic) path for glycosidic angle rotation 13– 18 or monosaccharide ring flipping, 19 in vacuo. ANOMERIC CARBON SERIESDuring the HSEA period, small-molecule modeling methods, such as the molecular mechanics (MM2/MM3) series of programs, were also adapted for use with carbohydrates. While the protein modeling community largely adopted molecular dynamics (MD) simulations, the carbohydrate community was influenced by the HSEA/Monte Carlo strategy well into the 1990s. 1 These early studies provided much insight into the basic properties of disaccharide linkages and were analogous in some aspects to the empirical conformational energy program for peptides (ECEPP) force field for modeling proteins, in which only the interresidue backbone angles were treated as flexible. Carbohydrate modeling approaches such as geometry of saccharides (GESA) 6 and geometry of glycopeptides (GEGOP) 3 treated the monosaccharides as rigid, and focused on searching the conformational space defined by the glycosidic angles using Monte Carlo sampling, 5 adopting the hard-sphere exo-anomeric (HSEA) force field. 1– 5 And the approaches (protocols and force fields, for example) for modeling carbohydrates initially evolved independently from the concurrent development of biomolecular force fields for modeling proteins and nucleic acids. Historically, the development of carbohydrate modeling methods was motivated by a desire to interpret solution data from nuclear magnetic resonance (NMR) spectroscopy in terms of three-dimensional (3D) structure. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. 2312)/RD/Type/Annot/AP>endobj84 0 obj/ProcSet>/Type/XObject/BBox/FormType 1>streamĮndstreamendobj85 0 objendobj86 0 objendobj87 0 objendobj88 0 objendobj89 0 objendobj90 0 objstream
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