GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.

As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact site...

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Autores principales:	Yu Xue, Zexian Liu, Xinjiao Gao, Changjiang Jin, Longping Wen, Xuebiao Yao, Jian Ren
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a8d01387e434c8db9decb5b75dbe876
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spelling	oai:doaj.org-article:5a8d01387e434c8db9decb5b75dbe8762021-12-02T20:20:32ZGPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.1932-620310.1371/journal.pone.0011290https://doaj.org/article/5a8d01387e434c8db9decb5b75dbe8762010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20585580/?tool=EBIhttps://doaj.org/toc/1932-6203As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at: http://sno.biocuckoo.org/.Yu XueZexian LiuXinjiao GaoChangjiang JinLongping WenXuebiao YaoJian RenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 6, p e11290 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Xue Zexian Liu Xinjiao Gao Changjiang Jin Longping Wen Xuebiao Yao Jian Ren GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
description	As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at: http://sno.biocuckoo.org/.
format	article
author	Yu Xue Zexian Liu Xinjiao Gao Changjiang Jin Longping Wen Xuebiao Yao Jian Ren
author_facet	Yu Xue Zexian Liu Xinjiao Gao Changjiang Jin Longping Wen Xuebiao Yao Jian Ren
author_sort	Yu Xue
title	GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
title_short	GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
title_full	GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
title_fullStr	GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
title_full_unstemmed	GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.
title_sort	gps-sno: computational prediction of protein s-nitrosylation sites with a modified gps algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/5a8d01387e434c8db9decb5b75dbe876
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GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm.

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