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Protein Modifications in the RER

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Modification of secretory and transmembrane proteins entering the rough ER begins in the ER lumen. These modifications aid in protein folding and stabilize the acquired tertiary structure. Protein modifications in the rough ER co-occur at different stages of protein folding.
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Related Experiment Video

Updated: Jan 31, 2026

Purification of Tubulin with Controlled Posttranslational Modifications and Isotypes from Limited Sources by Polymerization-Depolymerization Cycles

Purification of Tubulin with Controlled Posttranslational Modifications and Isotypes from Limited Sources by Polymerization-Depolymerization Cycles

Published on: November 5, 2020

Research progress in protein posttranslational modification site prediction.

Wenying He¹, Leyi Wei¹, Quan Zou^1,2

¹School of Computer Science and Technology, Tianjin University, Tianjin, China.

Briefings in Functional Genomics

|December 22, 2018

Summary

This summary is machine-generated.

Identifying posttranslational modifications (PTMs) is crucial for understanding cell biology. Machine learning tools offer rapid PTM prediction, aiding research and disease treatment development.

Keywords:

glycosylation machine learning posttranslational modification ubiquitination

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Area of Science:

Biochemistry
Bioinformatics
Computational Biology

Background:

Posttranslational modifications (PTMs) are critical regulators of protein function, impacting nearly all cellular processes.
Identifying PTMs is fundamental for understanding cell biology mechanisms and developing disease treatments.

Purpose of the Study:

To review bioinformatics tools for PTM prediction published since 2008.
To summarize prediction approaches for ubiquitination and glycosylation sites.
To discuss current challenges and future directions in PTM bioinformatics.

Main Methods:

Manual curation of bioinformatics tools.
Review of machine learning approaches for PTM site prediction.
Synthesis of information on ubiquitination and glycosylation site prediction.

Main Results:

A curated list of PTM bioinformatics tools is presented.
Common strategies for predicting ubiquitination and glycosylation sites are summarized.
Key challenges and limitations of existing PTM prediction tools are identified.

Conclusions:

Bioinformatics tools offer efficient alternatives to experimental PTM identification.
Further development is needed to address the challenges in current PTM prediction tools.
Future research should focus on improving accuracy and expanding the scope of PTM prediction.