SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.

S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and...

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Autores principales:	Tzong-Yi Lee, Yi-Ju Chen, Tsung-Cheng Lu, Hsien-Da Huang, Yu-Ju Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bd872931db554a0fbfb1c5e442620fa9
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spelling	oai:doaj.org-article:bd872931db554a0fbfb1c5e442620fa92021-11-18T06:50:13ZSNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.1932-620310.1371/journal.pone.0021849https://doaj.org/article/bd872931db554a0fbfb1c5e442620fa92011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21789187/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences.Tzong-Yi LeeYi-Ju ChenTsung-Cheng LuHsien-Da HuangYu-Ju ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 7, p e21849 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tzong-Yi Lee Yi-Ju Chen Tsung-Cheng Lu Hsien-Da Huang Yu-Ju Chen SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
description	S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences.
format	article
author	Tzong-Yi Lee Yi-Ju Chen Tsung-Cheng Lu Hsien-Da Huang Yu-Ju Chen
author_facet	Tzong-Yi Lee Yi-Ju Chen Tsung-Cheng Lu Hsien-Da Huang Yu-Ju Chen
author_sort	Tzong-Yi Lee
title	SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
title_short	SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
title_full	SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
title_fullStr	SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
title_full_unstemmed	SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
title_sort	snosite: exploiting maximal dependence decomposition to identify cysteine s-nitrosylation with substrate site specificity.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/bd872931db554a0fbfb1c5e442620fa9
work_keys_str_mv	AT tzongyilee snositeexploitingmaximaldependencedecompositiontoidentifycysteinesnitrosylationwithsubstratesitespecificity AT yijuchen snositeexploitingmaximaldependencedecompositiontoidentifycysteinesnitrosylationwithsubstratesitespecificity AT tsungchenglu snositeexploitingmaximaldependencedecompositiontoidentifycysteinesnitrosylationwithsubstratesitespecificity AT hsiendahuang snositeexploitingmaximaldependencedecompositiontoidentifycysteinesnitrosylationwithsubstratesitespecificity AT yujuchen snositeexploitingmaximaldependencedecompositiontoidentifycysteinesnitrosylationwithsubstratesitespecificity
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SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.

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