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Mining for protein S-sulfenylation in Arabidopsis uncovers redox-sensitive sites.

ABSTRACT: Hydrogen peroxide (H2O2) is an important messenger molecule for diverse cellular processes. H2O2 oxidizes proteinaceous cysteinyl thiols to sulfenic acid, also known as S-sulfenylation, thereby affecting the protein conformation and functionality. Although many proteins have been identified as S-sulfenylation targets in plants, site-specific mapping and quantification remain largely unexplored. By means of a peptide-centric chemoproteomics approach, we mapped 1,537 S-sulfenylated sites on more than 1,000 proteins in Arabidopsis thaliana cells. Proteins involved in RNA homeostasis and metabolism were identified as hotspots for S-sulfenylation. Moreover, S-sulfenylation frequently occurred on cysteines located at catalytic sites of enzymes or on cysteines involved in metal binding, hinting at a direct mode of action for redox regulation. Comparison of human and Arabidopsis S-sulfenylation datasets provided 155 conserved S-sulfenylated cysteines, including Cys181 of the Arabidopsis MITOGEN-ACTIVATED PROTEIN KINASE4 (AtMAPK4) that corresponds to Cys161 in the human MAPK1, which has been identified previously as being S-sulfenylated. We show that, by replacing Cys181 of recombinant AtMAPK4 by a redox-insensitive serine residue, the kinase activity decreased, indicating the importance of this noncatalytic cysteine for the kinase mechanism. Altogether, we quantitatively mapped the S-sulfenylated cysteines in Arabidopsis cells under H2O2 stress and thereby generated a comprehensive view on the S-sulfenylation landscape that will facilitate downstream plant redox studies.

SUBMITTER: Huang J

PROVIDER: S-EPMC6800386 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Mining for protein S-sulfenylation in Arabidopsis uncovers redox-sensitive sites.

Huang Jingjing J Willems Patrick P Wei Bo B Tian Caiping C Ferreira Renan B RB Bodra Nandita N Martínez Gache Santiago Agustín SA Wahni Khadija K Liu Keke K Vertommen Didier D Gevaert Kris K Carroll Kate S KS Van Montagu Marc M Yang Jing J Van Breusegem Frank F Messens Joris J

Proceedings of the National Academy of Sciences of the United States of America 20191002 42

Hydrogen peroxide (H2O2) is an important messenger molecule for diverse cellular processes. H2O2 oxidizes proteinaceous cysteinyl thiols to sulfenic acid, also known as S-sulfenylation, thereby affecting the protein conformation and functionality. Although many proteins have been identified as S-sulfenylation targets in plants, site-specific mapping and quantification remain largely unexplored. By means of a peptide-centric chemoproteomics approach, we ...[more]

PMID: 31578252

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Project description:Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew's correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.

Dataset Information

Mining for protein S-sulfenylation in Arabidopsis uncovers redox-sensitive sites.

Publications

Mining for protein S-sulfenylation in <i>Arabidopsis</i> uncovers redox-sensitive sites.

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