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Related Concept Videos

Protein Glycosylation01:25

Protein Glycosylation

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Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
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Glycocalyx and its Functions01:14

Glycocalyx and its Functions

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The glycocalyx is a carbohydrate-rich, fuzzy-appearing layer on the outer surface of the cell membrane. It is highly hydrophilic, because of this it attracts large amounts of water to the cell's surface. This aids the cell's interaction with the watery environment and also helps it to obtain substances dissolved in the water. It is also important for cell identification, self/non-self determination, and embryonic development and is used in cell-to-cell attachments to form tissues.
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Glycosaminoglycans01:23

Glycosaminoglycans

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Glycosaminoglycans (GAGs), also known as mucopolysaccharides, are long and linear polymers comprising of specific repeating disaccharides - the amino sugar that can be N-acetylglucosamine or N-acetylgalactosamine, and a uronic acid that is usually glucuronic acid or iduronic acid.
GAGS are found in the extracellular matrix of vertebrates, invertebrates, and bacteria. Due to their polar nature they attract water, and serve as excellent lubricants or shock absorbers in an animal body.
Hyaluronic...
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Proteoglycans01:05

Proteoglycans

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Glycans, a class of complex heterogeneous molecules, can be covalently attached to proteins to form glycosylated proteins that regulate various physiological and pathological processes. Glycosylated proteins or glycoproteins comprise N-linked and O-linked oligosaccharides. O-glycosylation is the most common type of protein glycosylation. Here, glycans attach to the oxygen atom of the hydroxyl groups of Serine or Threonine residues. O-linked glycosylation occurs later in protein processing,...
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Oligosaccharide Assembly01:24

Oligosaccharide Assembly

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Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
Multiple sugar molecules that may or may...
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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions
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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

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Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions.

Daniel Bojar1, Rani K Powers1, Diogo M Camacho2

  • 1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Cell Host & Microbe
|October 29, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning models analyze glycan evolution to predict pathogen interactions and functions. This research enhances understanding of host-microbe dynamics, glycan immunogenicity, and bacterial pathogenicity.

Keywords:
bioinformaticsdeep learningglycansglycobiologyhost-microbemachine learning

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Area of Science:

  • Carbohydrate Chemistry
  • Bioinformatics
  • Machine Learning

Background:

  • Glycans, the most diverse biopolymers, are crucial in host-microbe interactions.
  • Evolutionary pressures shape glycan structures, influencing interactions with pathogens and commensals.

Purpose of the Study:

  • To develop machine learning and bioinformatics methods for analyzing glycan evolutionary information.
  • To gain insights into pathogen-host interactions and predict glycan functions.

Main Methods:

  • Utilized natural language processing techniques to develop deep-learning models for glycans.
  • Trained models on a dataset of 19,299 unique glycans.
  • Developed glycan-alignment methods to analyze virulence-determining glycan motifs.

Main Results:

  • Deep-learning models can predict glycan immunogenicity and bacterial pathogenicity.
  • Identified glycan-mediated immune evasion mechanisms, including molecular mimicry.
  • Analyzed virulence-determining glycan motifs in bacterial capsular polysaccharides.

Conclusions:

  • The developed resources facilitate the identification and study of glycan motifs involved in host-microbe interactions.
  • Expanded understanding of glycan roles in immunogenicity, pathogenicity, molecular mimicry, and immune evasion.