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Predicting S-nitrosylation proteins and sites by fusing multiple features.

Wang-Ren Qiu1, Qian-Kun Wang1, Meng-Yue Guan1

  • 1School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
PubMed
Summary

This study introduces two computational models to identify S-nitrosylation proteins and predict S-nitrosylation sites. These models leverage sequence features and machine learning for improved accuracy in post-translational modification analysis.

Keywords:
S-nitrosylationidentificationmultiple featurespost-translational modificationrandom forest

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Area of Science:

  • Biochemistry and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • Protein S-nitrosylation is a critical post-translational modification involved in numerous biological processes.
  • Accurate identification and site prediction of S-nitrosylation are vital for basic research and drug development.

Purpose of the Study:

  • To develop computational models for identifying S-nitrosylation proteins.
  • To predict specific S-nitrosylation sites within protein sequences.

Main Methods:

  • Feature extraction using KNN scoring, Pseudo Amino Acid Composition (PseAAC), and bag-of-words models.
  • Data balancing with Synthetic Minority Oversampling Technique (SMOTE) and application of advanced classifiers.
  • Development of a site prediction model using Tripeptide Composition (TPC) and Composition of k-spaced amino acid pairs (CKSAAP), with feature selection via elastic nets.

Main Results:

  • The S-nitrosylation protein prediction model achieved 81.84% accuracy, 0.5178 MCC, and 0.8635 AUC in five-fold cross-validation.
  • The S-nitrosylation site prediction model demonstrated promising success rates.
  • A user-friendly web server, RF-SNOPS, was developed and made available online.

Conclusions:

  • The proposed models offer effective computational tools for S-nitrosylation protein identification and site prediction.
  • These tools can significantly aid researchers in basic science and drug discovery.
  • The RF-SNOPS web server provides accessible functionality for the scientific community.