Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Glycosylation

Protein Glycosylation

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...

Oligosaccharide Assembly

Oligosaccharide Assembly

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...

Proteoglycans

Proteoglycans

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,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

Journal of the American Medical Informatics Association : JAMIA·2024

Same author

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2.

Otology & neurotology open·2024

Same author

Potassium iodide enhances the killing effect of methylene blue mediated photodynamic therapy against F. monophora.

Photodiagnosis and photodynamic therapy·2024

Same author

Successful management of chromoblastomycosis utilizing conventional antifungal agents and imiquimod therapy.

Annals of clinical microbiology and antimicrobials·2024

Same author

Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation Study.

JMIR AI·2024

Same author

Modulation of copper sites in porphyrin metal-organic frameworks for electrochemical ascorbic acid sensing.

Chemical communications (Cambridge, England)·2024

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

Profiling of Permethylated Mucin O-glycans Using Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry

Profiling of Permethylated Mucin O-glycans Using Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry

Published on: June 20, 2025

Support vector machine-based mucin-type o-linked glycosylation site prediction using enhanced sequence feature

Manabu Torii¹, Hongfang Liu, Zhang-Zhi Hu

¹ISIS Center.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

This summary is machine-generated.

Predicting protein glycosylation sites is crucial for understanding protein function. A new method, Composition of Monomer Spectrum (CMS), improves computational prediction accuracy for these important post-translational modifications.

More Related Videos

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Published on: January 20, 2022

Related Experiment Videos

Last Updated: Jun 14, 2026

Profiling of Permethylated Mucin O-glycans Using Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry

Profiling of Permethylated Mucin O-glycans Using Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry

Published on: June 20, 2025

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Published on: January 20, 2022

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Glycosylation is a vital protein post-translational modification (PTM) with significant biological roles.
Mucin-type O-linked glycosylation is particularly prevalent and functionally important.
Accurate prediction of glycosylation sites is needed for protein annotation and functional studies.

Purpose of the Study:

To evaluate existing and develop enhanced computational methods for predicting protein glycosylation sites.
To assess the effectiveness of different protein fragment encoding strategies for machine learning models.

Main Methods:

Machine learning, specifically Support Vector Machines (SVMs), was employed for PTM site prediction.
Protein fragments were encoded into feature vectors using existing methods and a novel approach, Composition of Monomer Spectrum (CMS).

Main Results:

SVMs utilizing existing encoding methods achieved an Area Under the ROC Curve (AUC) of 90.3-91.3%.
The enhanced CMS encoding method resulted in a higher AUC of 92.4% for glycosylation site prediction.
Analysis indicates potential for further improvements in prediction accuracy through encoding strategies.

Conclusions:

The Composition of Monomer Spectrum (CMS) offers improved accuracy for computational prediction of mucin-type O-linked glycosylation sites.
Encoding strategies significantly impact the performance of machine learning models in PTM prediction.
Further research into encoding methods can enhance the accuracy of protein glycosylation site prediction.