Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana

Nitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation—the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulat...

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Autores: Chaki, Mounira, Kovacs, Izabella, Spannagl, Manuel, Lindermayr, Christian
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4049
Acceso en línea:https://hdl.handle.net/10953/4049
Access Level:acceso abierto
Palabra clave:Nitric oxide
S-nitrosylation
Arabidopsis
GPS-SNO
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spelling Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thalianaChaki, MouniraKovacs, IzabellaSpannagl, ManuelLindermayr, ChristianNitric oxideS-nitrosylationArabidopsisGPS-SNONitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation—the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulation of protein functions is well established, its substrate specificity remains unknown. Identification of candidates for S-nitrosylation and their target cysteine residues is fundamental for studying the molecular mechanisms and regulatory roles of S-nitrosylation in plants. Several experimental methods that are based on the biotin switch have been developed to identify target proteins for S-nitrosylation. However, these methods have their limits. Thus, computational methods are attracting considerable attention for the identification of modification sites in proteins. Using GPS-SNO version 1.0, a recently developed S-nitrosylation site-prediction program, a set of 16,610 candidate proteins for S-nitrosylation containing 31,900 S-nitrosylation sites was isolated from the entire Arabidopsis proteome using the medium threshold. In the compartments ‘‘chloroplast,’’ ‘‘CUL4-RING ubiquitin ligase complex,’’ and ‘‘membrane’’ more than 70% of the proteins were identified as candidates for S-nitrosylation. The high number of identified candidates in the proteome reflects the importance of redox signaling in these compartments. An analysis of the functional distribution of the predicted candidates showed that proteins involved in signaling processes exhibited the highest prediction rate. In a set of 46 proteins, where 53 putative S-nitrosylation sites were already experimentally determined, the GPS-SNO program predicted 60 S-nitrosylation sites, but only 11 overlap with the results of the experimental approach. In general, a computer-assisted method for the prediction of targets for S-nitrosylation is a very good tool; however, further development, such as including the three dimensional structure of proteins in such analyses, would improve the identification of S-nitrosylation sites.This work was supported by Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme (Call: FP7-PEOPLE-2011-IEF) under grant agreement nº300176 and the Bundesministerium fur Forschung und Bildung.PUBLIC LIBRARY SCIENCE202520252014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10953/4049reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésPlos One [2014]; [9 (10)]: [1-12]info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/40492026-06-24T12:41:07Z
dc.title.none.fl_str_mv Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
title Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
spellingShingle Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
Chaki, Mounira
Nitric oxide
S-nitrosylation
Arabidopsis
GPS-SNO
title_short Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
title_full Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
title_fullStr Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
title_full_unstemmed Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
title_sort Computational Prediction of Candidate Proteins for S-Nitrosylation in Arabidopsis thaliana
dc.creator.none.fl_str_mv Chaki, Mounira
Kovacs, Izabella
Spannagl, Manuel
Lindermayr, Christian
author Chaki, Mounira
author_facet Chaki, Mounira
Kovacs, Izabella
Spannagl, Manuel
Lindermayr, Christian
author_role author
author2 Kovacs, Izabella
Spannagl, Manuel
Lindermayr, Christian
author2_role author
author
author
dc.subject.none.fl_str_mv Nitric oxide
S-nitrosylation
Arabidopsis
GPS-SNO
topic Nitric oxide
S-nitrosylation
Arabidopsis
GPS-SNO
description Nitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation—the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulation of protein functions is well established, its substrate specificity remains unknown. Identification of candidates for S-nitrosylation and their target cysteine residues is fundamental for studying the molecular mechanisms and regulatory roles of S-nitrosylation in plants. Several experimental methods that are based on the biotin switch have been developed to identify target proteins for S-nitrosylation. However, these methods have their limits. Thus, computational methods are attracting considerable attention for the identification of modification sites in proteins. Using GPS-SNO version 1.0, a recently developed S-nitrosylation site-prediction program, a set of 16,610 candidate proteins for S-nitrosylation containing 31,900 S-nitrosylation sites was isolated from the entire Arabidopsis proteome using the medium threshold. In the compartments ‘‘chloroplast,’’ ‘‘CUL4-RING ubiquitin ligase complex,’’ and ‘‘membrane’’ more than 70% of the proteins were identified as candidates for S-nitrosylation. The high number of identified candidates in the proteome reflects the importance of redox signaling in these compartments. An analysis of the functional distribution of the predicted candidates showed that proteins involved in signaling processes exhibited the highest prediction rate. In a set of 46 proteins, where 53 putative S-nitrosylation sites were already experimentally determined, the GPS-SNO program predicted 60 S-nitrosylation sites, but only 11 overlap with the results of the experimental approach. In general, a computer-assisted method for the prediction of targets for S-nitrosylation is a very good tool; however, further development, such as including the three dimensional structure of proteins in such analyses, would improve the identification of S-nitrosylation sites.
publishDate 2014
dc.date.none.fl_str_mv 2014
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10953/4049
url https://hdl.handle.net/10953/4049
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Plos One [2014]; [9 (10)]: [1-12]
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv PUBLIC LIBRARY SCIENCE
publisher.none.fl_str_mv PUBLIC LIBRARY SCIENCE
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
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