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Related Concept Videos

Protein Glycosylation01:25

Protein Glycosylation

7.0K
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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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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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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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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Glycocalyx and its Functions01:14

Glycocalyx and its Functions

4.2K
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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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions
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Can ChatGPT pass Glycobiology?

Devin Ormsby Williams1, Elisa Fadda1,2

  • 1Department of Chemistry.

Glycobiology
|August 2, 2023
PubMed
Summary

Large Language Models (LLMs) show potential in scientific writing but require careful evaluation for accuracy and appropriate use in higher education. This study assesses ChatGPT

Area of Science:

  • Carbohydrate Chemistry
  • Glycobiology
  • Scientific Writing

Background:

  • Interactive Large Language Models (LLMs) have rapidly gained global attention since late 2022.
  • Discussions on LLMs' potential benefits and risks are prominent, especially in higher education concerning academic integrity.
  • Concerns exist regarding student cheating and researcher plagiarism due to uncontrolled LLM use.

Purpose of the Study:

  • To systematically evaluate the capabilities of ChatGPT in scientific writing tasks.
  • To analyze ChatGPT's performance on progressively complex scientific writing and exam-type questions.
  • To assess ChatGPT's utility from the perspectives of a student, researcher, and lecturer.

Main Methods:

  • ChatGPT was tested on scientific writing tasks and exam-type questions in Carbohydrate Chemistry and Glycobiology.
Keywords:
AIChatGPTGlycobiologyLLMhigher education

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  • The evaluation considered progressively complex queries to gauge the model's performance.
  • Results were analyzed from multiple academic viewpoints.

    • Main Results:

    • Identified strengths and limitations of ChatGPT in providing accurate scientific information.
    • Determined optimal query formats and complexity for desired outputs.
    • Gained insights into integrating LLMs into teaching and learning strategies.

    Conclusions:

    • LLMs like ChatGPT have specific strengths and weaknesses in scientific communication.
    • Effective integration requires understanding query optimization and potential limitations.
    • Strategies for responsible LLM use in education and research are crucial.