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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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Conservation of Protein Domains Over Different Proteins

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Related Experiment Video

Updated: Sep 8, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Subash C Pakhrin¹, Suresh Pokharel², Hiroto Saigo³

¹School of Computing, College of Engineering, Wichita State University, Wichita, KS, USA.

Methods in Molecular Biology (Clifton, N.J.)

|June 13, 2022

Summary

This summary is machine-generated.

Deep learning (DL) advances computational prediction of posttranslational modifications (PTMs). This review highlights DL approaches for PTM site and proteolytic cleavage prediction, offering future research directions.

Keywords:

Deep learning Machine learning Phosphorylation Posttranslational modification site Proteolytic cleavage

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Area of Science:

Biochemistry
Computational Biology
Proteomics

Background:

Posttranslational modifications (PTMs) create proteomic diversity in all organisms.
PTMs include covalent modifications and proteolytic cleavage, crucial for biological understanding.
Experimental methods like mass spectrometry aid PTM characterization, but computational approaches are needed for over 450 PTM types.

Purpose of the Study:

To review recent deep learning (DL) based computational approaches for PTM site prediction.
To review recent advances in DL for proteolytic cleavage prediction, a less-studied PTM.
To highlight DL architectures, feature encoding, novelty, and tool availability for PTM prediction.

Main Methods:

Review of recent literature on DL-based PTM prediction.
Analysis of DL architectures, feature encoding strategies, and novelty of presented approaches.
Assessment of the availability of computational tools and resources for PTM prediction.

Main Results:

Numerous DL-based methods have emerged for PTM site prediction.
Significant progress has been made in DL for proteolytic cleavage prediction.
Key aspects such as DL architecture and feature encoding are crucial for prediction accuracy.

Conclusions:

DL approaches are increasingly vital for understanding and predicting PTMs.
Further research is needed to explore novel DL architectures and applications in PTM prediction.
The development and accessibility of DL tools will accelerate PTM research.