Background: S-glutathionylation is the formation of disulfide bonds between the
tripeptide glutathione and cysteine residues of the protein, protecting them from
irreversible oxidation and in some cases causing change in their functions. Regulatory
glutathionylation of proteins is a controllable and reversible process associated with cell
response to the changing redox status. Prediction of cysteine residues that undergo
glutathionylation allows us to find new target proteins, which function can be altered
in pathologies associated with impaired redox status. We set out to analyze this issue
and create new tool for predicting S-glutathionylated cysteine residues.
Results: One hundred forty proteins with experimentally proven S-glutathionylated
cysteine residues were found in the literature and the RedoxDB database. These
proteins contain 1018 non-S-glutathionylated cysteines and 235 S-glutathionylated
ones. Based on 235 S-glutathionylated cysteines, non-redundant positive dataset of 221
heptapeptide sequences of S-glutathionylated cysteines was made. Based on 221
heptapeptide sequences, a position-specific matrix was created by analyzing the
protein sequence near the cysteine residue (three amino acid residues before and three
after the cysteine). We propose the method for calculating the glutathionylation
propensity score, which utilizes the position-specific matrix and a criterion for
predicting glutathionylated peptides.
Conclusion: Non-S-glutathionylated sites were enriched by cysteines in − 3 and + 3
positions. The proposed prediction method demonstrates 76.6% of correct predictions
of S-glutathionylated cysteines. This method can be used for detecting new
glutathionylation sites, especially in proteins with an unknown structure.