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GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate
Yi-Ju Chen1, Cheng-Tsung Lu2, Kai-Yao Huang2
1Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
Plos One
|April 8, 2015
Summary
This study identifies sequence motifs that predict protein S-glutathionylation sites, a key post-translational modification. The findings enable better understanding and prediction of this crucial regulatory process.
Area of Science:
- Biochemistry
- Proteomics
- Bioinformatics
Background:
- S-glutathionylation is a reversible protein modification regulating cellular functions.
- The substrate specificity of S-glutathionylation is currently unknown.
- Understanding this specificity is crucial for deciphering protein regulation and signaling.
Purpose of the Study:
- To investigate the structural factors influencing S-glutathionylation site specificity.
- To develop a predictive model for identifying S-glutathionylation sites.
- To create a web-based tool for predicting uncharacterized S-glutathionylation sites.
Main Methods:
- Analysis of 1783 experimentally identified S-glutathionylation sites.
- Investigated flanking amino acid composition and accessible surface area (ASA).
- Employed statistical methods for motif detection and Support Vector Machines (SVM) for predictive modeling.
Main Results:
- Positively charged amino acids flanking cysteine residues may promote S-glutathionylation.
- SVM models trained with substrate motifs achieved high accuracy in prediction.
- Successfully identified known S-glutathionylation sites in thioredoxin and PTP1B.
Conclusions:
- Identified conserved substrate motifs for S-glutathionylation.
- Developed an accurate SVM-based prediction model for S-glutathionylation sites.
- Created the GSHSite web tool for predicting S-glutathionylation sites in protein sequences.
