A novel approach for predicting protein S-glutathionylation
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RESEARCH
Open Access
A novel approach for predicting protein Sglutathionylation Anastasia A. Anashkina1* , Yuri M. Poluektov1, Vladimir A. Dmitriev1, Eugene N. Kuznetsov2, Vladimir A. Mitkevich1, Alexander A. Makarov1 and Irina Yu. Petrushanko1* From 11th International Young Scientists School “Systems Biology and Bioinformatics” – SBB-2019 Novosibirsk, Russia. 24-28 June 2019
* Correspondence: [email protected]; [email protected] 1 Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991 Moscow, Russia Full list of author information is available at the end of the article
Abstract 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. Keywords: S-glutathionylation, Peptide sequence, Protein glutathionylation, Redox modification, Prediction method, Glutathionylation propensity score
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