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Updated: Jun 4, 2026

Detection of Protein S-Acylation using Acyl-Resin Assisted Capture
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An efficient support vector machine approach for identifying protein S-nitrosylation sites.

Yu-Xin Li1, Yuan-Hai Shao, Ling Jing

  • 1College of Science, China Agricultural University, Beijing 100083, China.

Protein and Peptide Letters
|January 29, 2011
PubMed
Summary

A new computational tool, CPR-SNO, accurately predicts protein S-nitrosylation sites. This method significantly improves upon existing predictors, aiding research into cellular processes.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Protein S-nitrosylation is vital for cellular functions.
  • Identifying S-nitrosylation sites is crucial but challenging with traditional methods.
  • Existing computational predictors for S-nitrosylation sites lack sufficient accuracy.

Purpose of the Study:

  • To develop a more accurate computational method for predicting protein S-nitrosylation sites.
  • To introduce CPR-SNO, an efficient predictor utilizing a novel encoding scheme.

Main Methods:

  • Employed support vector machine (SVM) for prediction.
  • Evaluated six different encoding schemes.
  • Developed CPR-SNO using a coupling patterns based encoding scheme.

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Last Updated: Jun 4, 2026

Detection of Protein S-Acylation using Acyl-Resin Assisted Capture
08:31

Detection of Protein S-Acylation using Acyl-Resin Assisted Capture

Published on: April 10, 2020

Nitropeptide Profiling and Identification Illustrated by Angiotensin II
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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

Main Results:

  • CPR-SNO achieved an AUC of 0.8289 in 10-fold cross-validation.
  • This performance significantly surpasses the previous best method (GPS-SNO 1.0) with an AUC of 0.685.
  • CPR-SNO demonstrated strong predictive performance in large-scale substrate annotation.

Conclusions:

  • CPR-SNO offers a highly accurate and efficient computational approach for S-nitrosylation site prediction.
  • The tool provides a valuable resource for researchers studying biological mechanisms involving S-nitrosylation.
  • CPR-SNO is available as a web server for the scientific community.