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Multifaceted Computational Modeling in Glycoscience.

Serge Perez1, Olga Makshakova2

  • 1Centre de Recherche sur les Macromolecules Vegetales, University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041, France.

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This summary is machine-generated.

Glycoscience utilizes computational modeling to explore the structure and function of carbohydrates and glycans. This review details methods and applications, including their role in viral infections like SARS-CoV-2.

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Area of Science:

  • Glycoscience
  • Computational Biology
  • Structural Biology

Background:

  • Carbohydrates and glycans are abundant and essential molecules in all organisms.
  • Understanding their 3D structure and dynamics is crucial for elucidating biological functions.
  • Computational methods are vital for studying these complex molecules.

Purpose of the Study:

  • To provide a comprehensive overview of computational modeling applications in glycoscience.
  • To illustrate the use of computational methods in understanding carbohydrate interactions and structures.
  • To highlight the role of glycans in diseases, including SARS-CoV-2.

Main Methods:

  • Quantum chemistry
  • Molecular dynamics and mechanics
  • Coarse-grained modeling
  • Docking calculations
  • Mesoscale modeling

Main Results:

  • Detailed presentations of computational modeling applications.
  • Illustrations of carbohydrate-carbohydrate interactions, glycolipids, and N-/O-linked glycans (including SARS-CoV-2 relevance).
  • Analysis of polysaccharide and lipopolysaccharide structures.
  • Exploration of protein-carbohydrate interactions (enzymes, lectins, antibodies, etc.).

Conclusions:

  • Computational modeling is indispensable for advancing glycoscience.
  • The review provides a valuable resource of tools and databases for structural glycobioinformatics.
  • Further research using these methods will deepen our understanding of biological systems.