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Protein Modifications in the RER01:26

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.
Broadly, these modifications can be categorized into four main categories — glycosylation, formation of disulfide bonds, assembly of protein subunits, and specific proteolytic cleavages like removal of signal...
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pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.

Shihua Li1,2, Kai Yu1, Guandi Wu1

  • 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.

Frontiers in Cell and Developmental Biology
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

We developed pCysMod, a deep learning tool for predicting multiple protein cysteine modifications like S-nitrosylation and S-palmitoylation. This computational approach offers a faster, cost-effective alternative to experimental methods for identifying crucial regulatory sites.

Keywords:
deep learningfeature extractionpost-translational modificationspredictionprotein cysteine modifications

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Cysteine residues undergo various post-translational modifications (PTMs) that regulate cellular redox homeostasis and signaling pathways.
  • Accurate identification of protein cysteine modifications is essential for understanding their regulatory roles.
  • Experimental methods for PTM identification are often time-consuming and labor-intensive, driving interest in computational approaches.

Purpose of the Study:

  • To develop a novel, comprehensive deep learning tool for predicting multiple types of protein cysteine modifications.
  • To provide a convenient and cost-effective computational method for identifying potential cysteine modification sites.
  • To establish a web server for easy access to the prediction tool for the research community.

Main Methods:

  • Development of a deep learning model, pCysMod, integrating various protein sequence features.
  • Optimization of model hyperparameters using particle swarm optimization algorithms.
  • Creation of a benchmark dataset by curating experimentally verified cysteine modification sites from literature and existing databases.

Main Results:

  • pCysMod demonstrated robust performance in predicting five types of cysteine modifications: S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation.
  • The model achieved high average AUC values, outperforming existing prediction tools.
  • A user-friendly web server (http://pcysmod.omicsbio.info) was established to facilitate protein cysteine modification site prediction.

Conclusions:

  • pCysMod is a stable and effective tool for predicting multiple protein cysteine modifications.
  • The developed computational approach significantly advances the study of protein cysteine modifications and their regulatory mechanisms.
  • The web server provides a valuable resource for researchers investigating cysteine modification biology.